“To  Err  on  the  Side  of  Caution:”  Ethical  Dimensions  of  the  National  Weather  Service  Warning  Process  

 Jennifer  J.  Henderson  

   

Dissertation  submitted  to  the  faculty  of  the  Virginia  Polytechnic  Institute  and  State  University  in  partial  fulfillment  of  the  requirements  

for  the  degree  of    

Doctor  of  Philosophy    in    

Science  and  Technology  Studies            

Saul  Halfon,  Co-­‐Chair  Gary  Lee  Downey,  Co-­‐Chair  

James  H.  Collier  Sonja  D.  Schmid  Rebecca  E.  Morss  

   

Dec.  6,  2016  Blacksburg,  VA  

 Keywords:  National  Weather  Service,  weather  warnings,  expertise,  

accuracy,  ethic  of  care                      

Copyright  2016,  Jennifer  J.  Henderson

“To  Err  on  the  Side  of  Caution:”  Ethical  Dimensions  of  the  National  Weather  Service  Warning  Process  

 Jennifer  J.  Henderson  

 ABSTRACT  

   This  dissertation  traces  three  ethical  dimensions,  or  values,  of  weather  warnings  in  the  National  Weather  Service  (NWS):  an  ethic  of  accuracy,  and  ethic  of  care,  and  an  ethic  of  resilience.  Each  appear  in  forecaster  work  but  are  not  equally  visible  in  the  identity  of  a  forecaster  as  scientific  expert.  Thus,  I  propose  that  the  NWS  should  consider  rethinking  its  science  through  its  relationship  to  multiple  publics,  creating  what  Sandra  Harding  calls  a  “strong  objectivity.”  To  this  end,  I  offer  the  concept  of  empathic  accuracy  as  an  ethic  that  reflects  the  interrelatedness  of  precision  and  care  that  already  attend  to  forecasting  work.  First,  I  offer  a  genealogy  of  the  ethic  of  accuracy  as  forecasters  see  it.  Beginning  in  the  1960s,  operational  meteorologists  mounted  an  ethic  of  accuracy  through  the  “man-­‐machine  mix,”  a  concept  that  pointed  to  an  identity  of  the  forecasting  scientist  that  required  a  demarcation  between  humans  and  technologies.  It  is  continually  troubled  by  the  growing  power  of  computer  models  to  make  predictions.  Second,  I  provide  an  ethnographic  account  of  the  concern  expressed  by  forecasters  for  their  publics.  I  do  so  to  demonstrate  how  an  ethic  of  care  exists  alongside  accuracy  in  their  forecasting  science,  especially  during  times  of  crisis.  I  recreate  the  concern  for  others  that  their  labor  performs.  It  is  an  account  that  values  emotion  and  is  sensitive  to  context,  showing  what  Virginia  Held  called  the  “self-­‐and-­‐other  together”  that  partially  constitutes  a  forecaster  identity.  Third,  I  critique  the  NWS  Weather  Ready  Nation  Roadmap  and  its  emphasis  on  developing  in  the  public  an  ethic  of  resilience.  I  argue  that,  as  currently  framed,  this  ethic  and  its  instantiation  in  the  initiative  Impact  Based  Decision  Support  Services  narrowly  defines  community  to  such  an  extent  that  it  disappears  the  public.  However,  it  also  reveals  other  valences  of  resilience  that  have  the  potential  to  open  up  a  space  for  an  empathetic  accuracy.    Finally,  I  close  with  a  co-­‐authored  article  that  explores  my  own  commitment  to  an  ethic  of  relationality  in  disaster  work  and  the  compromises  that  create  tension  in  me  as  a  scholar  and  critical  participant  in  the  weather  community.  

“To  Err  on  the  Side  of  Caution:”  Ethical  Dimensions  of  the  National  Weather  Service  Warning  Process  

 Jennifer  J.  Henderson  

 GENERAL  AUDIENCE  ABSTRACT  

   Every  year,  weather  disasters  affect  people’s  lives.  When  tornadoes,  flash  floods,  winter  weather,  and  heat  threaten  communities,  forecasters  in  the  National  Weather  Service  (NWS)  have  the  responsibility  to  issue  alerts,  which  are  called  warnings,  to  help  keep  people  safe  from  harm.  For  decades,  these  professionals  have  used  the  best  technologies  they  have—Doppler  radar,  satellites,  and  observation  networks—to  scan  the  skies  for  potential  danger.  And  they  have  done  so  diligently  and  with  great  attention  to  making  their  forecasts  and  warnings  as  accurate  as  possible.  Yet  each  year,  as  these  weather  phenomena  pose  risks  to  people  in  their  local  communities,  accuracy  of  warnings  is  not  enough  to  keep  people  safe.  This  dissertation  contributes  to  such  concerns.  Rather  than  focus  on  specific  technologies  that  might  be  improved,  I  explore  the  professional  identity  of  the  NWS  forecaster  and  potential  changes  to  their  science  that  might  help  them  meet  their  mission  to  protect  life.  I  offer  insight  into  how  NWS  forecasters  have  chosen  to  see  themselves  and  their  role  in  society,  and  why.    Specifically,  my  goal  is  to  explore  ways  that  the  agency’s  focus  on  accuracy  is  unintentionally  masking  other  values  that  are  important  to  the  professional  practices  and  activities  of  the  forecaster.  To  help  make  the  complexity  of  their  identities  more  apparent,  I  offer  a  new  kind  of  ethic,  an  empathetic  accuracy,  that  better  reflects  not  just  the  attention  forecasters  give  to  correct  predictions  but  predictions  done  with  care  and  concern  for  the  people  they  serve.  I  explore  the  history  of  the  term  accuracy  to  show  why  it  is  so  important  in  their  work;  I  show  how  the  notion  of  care  is  already  key  to  their  jobs;  and  I  critique  current  policies  that  may  either  diminish  or  enhance  their  relationships  with  people  in  the  general  public.  I  suggest  that  the  agency  should  consider  developing  a  better  kind  of  science  that  accounts  for  this  complex  professional  image  of  the  forecaster  as  scientist  and  public  servant.  More  importantly,  my  goal  is  to  show  that  NWS  forecasters  have  alternative  roles  they  can  engage  with  that  are  equally,  if  not  more  important,  to  the  people  whose  lives  they  are  committed  to  protecting.  

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Dedication  This  dissertation  is  dedicated  to  my  mom,  Joyce  Marie  Andrus  Henderson,  who  inspires  me  every  day.  She  has  continually  shown  me  by  example  what  it  means  to  be  good,  caring,  and  thoughtful.  She  is  the  most  ethically  minded  person  I  know.

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Acknowledgements    I  owe  the  best  parts  of  this  dissertation  to  so  many  people.    Dean  DePauw,  for  putting  me  on  a  path  to  explore  ethical  issues;  Skip  Fuhrman,  for  making  the  Ph.D.  possible;  Carol  Sue  Slusser  for  making  it  all  bearable.      Advisors  Saul  Halfon  and  Gary  Lee  Downey,  for  working  so  diligently  to  help  me  finish  and  finish  well;  Jim  Collier,  for  normative  insight;  Sonja  Schmid,  for  helping  me  see  the  multiplicities  of  risk;  and  Rebecca  Morss,  for  keeping  me  honest  about  weather  forecasting.      All  of  the  National  Weather  Service  forecasters  and  staff  I’ve  been  privileged  to  know,  learn  from,  observe,  and  befriend.  I  can’t  thank  you  enough  for  your  camaraderie  and  trust.  I  hope  that  this  dissertation  resonates  with  you  in  some  small  way.    Friends  and  colleagues  in  STS:  Crystal  Cook  Marshall,  Max  Liboiron,  Scott  Knowles,  Katrina  Petersen,  Phaedra  Daipha,  Vivian  Choi,  Kim  Fortun,  Adam  Smith,  Carol  Davis,  Sarv  Lotfi,  Melissa  Hafner,  Monique  Durfour,  Ashley  Shew,  William  Davis,  Keith  Johnson,  Josh  Brinkman,  Trevor  Croker,  and  Sumitra  Nair.  Faculty  members  in  STS  at  Virginia  Tech,  you  are  all  such  good  people.  Thank  you  for  everything.    My  NCAR  family:  Rebecca  Morss,  Julie  Demuth,  Heather  Lazrus,  and  Olya  Wilhelmi—for  showing  me  kindness  and  an  alternative  career  to  academia.      Friends  and  colleagues  in  the  weather  community:  Tom  LeFebvre,  for  being  so  generous  with  your  time  and  for  sharing  the  many  histories  of  forecasting  with  me.  Dave  Carroll,  Kevin  Myatt,  and  Chris  White  for  letting  me  participate  as  a  VT  storm  chaser  and  showing  me  why  such  efforts  matter.  Susan  Jasko,  Laura  Myers,  Vankita  Brown—my  Weather  Warriors,  for  all  your  love  and  friendship.  Eve  Gruntfest,  Jen  Spinney,  and  Dan  Nietfeld  for  always  believing  in  me.  Bill  Hooke,  for  seeing  the  possibilities  in  me  and  my  work.  Kelvin  Droegemier,  Lans  Rothfusz,  Gina  Eosco,  and  Kim  Klockow  for  inviting  me  to  be  part  of  vital  conversations  about  our  shared  commitment  to  the  Weather  Enterprise.  Laura  Furgione,  who  first  helped  me  enter  the  forecasting  world.    Russ  Schumacher  and  the  SPREAD  collective.  I  have  enjoyed  our  friendship  and  collaborations.  Randy  Wynne  and  my  IGEP  peeps,  thank  you  for  your  support.  AMS  Policy  Colloquium  family,  you  have  made  this  social  scientist  feel  welcome.    My  chosen  family:  Becca,  Julie,  Greg,  Chris  &  Kathe,  James  &  Sue,  David  &  Janet—I  think  of  you  all  the  time.  Thank  you  for  choosing  me  back.    

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My  siblings,  especially  my  sister,  Angela,  and  her  husband,  Devin;  my  brother,  Micah  and  his  wife  Tina;  and  my  brother,  Eddie.  You  are  the  core  of  my  world.  And  to  the  satellites  that  circle  us  in  their  curiosity  and  love,  my  nieces  and  nephew:  Topaz,  Chloe,  and  Lucas.  To  all  the  Websters,  especially  Sally,  my  second  mom;  Don  and  Shirl,  my  adopted  parents,  and  their  sweet  babies;  and  the  Wolfes,  Azure  and  Jeff.  I’m  so  lucky  to  have  you  all  in  my  life.    Thanks  to  my  dad,  who  helped  bring  me  into  the  world.  And  to  my  mom  for  making  it  mean  something.    To  the  crazy  kitty  kids  I  have  shared  with  my  partner,  Dane:  Lily,  Bumble,  Sissy,  Peanut,  and  Roo.  Thank  you  for  keeping  me  sane.    And  to  Dane,  my  world.  Thank  you.  Always.  There  are  no  words.      This  dissertation  was  funded,  in  part,  by  the  Interdisciplinary  Graduate  Education  Program  in  Remote  Sensing  at  Virginia  Tech;  an  Advanced  Study  Program  Visiting  Graduate  Student  Fellowship  from  the  National  Center  for  Atmospheric  Research;  and  a  VORTEX  Southeast  research  grant  from  the  National  Oceanic  and  Atmospheric  Administration  (NOAA  Award  NA15OAR4590233).    

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Table  of  Contents    Dedication  ................................................................................................................................  iv  

Acknowledgements  .................................................................................................................  v  

Table  of  Contents  ..................................................................................................................  vii  List  of  Figures  ........................................................................................................................  viii  

List  of  Tables  ............................................................................................................................  ix  Preface  .........................................................................................................................................  x  

Introduction  ...........................................................................................................................  xvi  

Article  1:  The  Ethic  of  Accuracy:  Troubles  in  The  Man-­‐Machine  Mix  ....................  1  Article  2:  Matters  of  Concern  ............................................................................................  59  

Article  3:  Weather  Ready  Nation  or  Ready  Weather  Agency?  Developing  an  Ethic  of  Resilience  in  the  National  Weather  Service  .................................................  96  

Article  4:  Compromise  and  Action:  Tactics  for  Doing  Ethical  Research  in  Disaster  Zones  .....................................................................................................................  136  Conclusion  ............................................................................................................................  165  

Bibliography  ........................................................................................................................  172  

Appendix  ...............................................................................................................................  190    

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List  of  Figures    Figure 1 Meteorological cancer of the man-machine mix according to Snellman ............ 30  Figure 2 Two forecaster roles as envisioned by Snellman. Top: Meteorological cancer of

the man-machine mix. Bottom: Rebalanced man-machine mix with AFOS. ........... 38    

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List  of  Tables      Table 1 Types of Accuracy in Weather Warnings ............................................................ 52    

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Preface    

    This  dissertation  has  brought  me  full  circle  in  many  ways.    

  In  2008,  I  joined  a  group  of  Virginia  Tech  undergraduates  who  were  completing  

their  field  studies  course  in  meteorology,  known  informally  as  the  “storm  chase  

experience.”  I  had  just  moved  from  Kansas  where  I  completed  my  studies  in  creative  

writing  and  where  had  fallen  in  love  both  with  the  green  lines  of  tall  grass  characteristic  of  

the  Plains  and  storms  that  blossomed  along  the  horizons  throughout  the  spring  and  

summer.  Tornados,  in  particular,  captured  my  attention.  Like  many  meteorologists  I  would  

later  interview,  I  found  the  spinning  winds  awe-­‐inspiring  and  terrible,  what  the  Romantic  

writers  in  nineteenth  century  England  called  the  Sublime.  Joining  the  storm  chase  group  as  

a  nonfiction  writer,  I  hoped  to  better  understand  what  motivated  novice  meteorologists  to  

take  such  risks  with  their  lives.  I  couldn’t  know  then  that  what  I  experienced  during  this  

trip  would  also  transform  my  career.    

  As  a  chase  group,  the  nine  undergraduate  students,  two  storm  chase  leaders,  and  I  

spent  sixteen  days  threading  storms  and  waiting  out  their  absence  as  we  made  our  way  

West  across  the  continent.  During  one  of  what  chasers  call  “down  days,”  or  days  when  

there  were  no  storms  to  chase,  we  visited  a  small  town  of  Saragosa,  Texas.  On  the  evening  

of  May  22,  1984,  the  community  had  endured  an  F-­‐4  tornado,  a  designation  of  the  Fujita  

Scale  that  signified  winds  at  somewhere  between  207-­‐260  miles  per  hour—what  Fujita,  the  

scientist  who  created  the  scale,  called  “devastating  damage.”1  Over  thirty  years  later,  the  

town  seemed  to  have  recovered  in  small  ways,  with  patchwork  repairs  to  some  buildings,   1  Fujita,  “A  Proposed  Characterization  of  Tornadoes  and  Hurricanes  by  Area  and  Intensity.”  

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though  just  as  many  had  been  abandoned.  A  few  foundations  stood  empty,  reminding  any  

visitor  of  the  power  of  such  storms  and  the  complexities  of  recovery.  Our  chase  leader  

would  tell  us  that  the  main  notice  the  Spanish-­‐speaking  community  had  of  the  impending  

storm  came  from  a  spotter  who  ran  into  a  building  where  a  Head  Start  graduation  

ceremony  was  underway  and  yelled,  “Tornado!”  They  never  received  the  official  warning  

issued  126  miles  away  at  a  forecasting  office  in  Midland.  People  raced  to  put  their  children  

under  tables  and  chairs  stacked  against  one  of  the  walls  of  the  cinderblock  building.  A  

woman  who  survived  that  day  recounted  to  us  how  she  lost  her  mother,  an  aunt,  and  a  

neighbor.  She  told  us  that  the  people  in  her  town  had  no  idea  the  tornado  was  coming,  and  

that  she  hoped  as  meteorology  students  that  our  group  would  do  more  to  ensure  that  a  

tragedy  like  the  one  she  lived  through  didn’t  happen  again.    

  Several  questions  came  to  mind  as  our  small  group  later  studied  the  stone  memorial  

just  outside  the  new  community  center,  which  was  dedicated  to  the  thirty  individuals  who  

lost  their  lives  that  day:  Why  didn’t  they  get  a  warning?  What  was  meant  by  “warning”  or  

“the  warning  system”?  Why  had  so  many  died?  And  who  or  what  was  responsible  for  what  

seemed  like  a  grave  miscommunication?  What  I  didn’t  know  then  is  that  these  questions  

would  lead  me  back  to  graduate  school  and  into  the  field  to  learn  about  the  warning  system  

and  different  participants  attending  to  it.  It  would  also  lead  me  to  become  an  active  and  

critical  participant  in  the  “Weather  Enterprise,”  or  a  network  of  agencies,  organizations,  

and  industries  from  three  broad  sectors:    meteorologists  from  the  private  sector,  such  as  

broadcast  meteorologists  and  those  in  industry;  meteorologists  from  the  public  sector,  

such  as  the  National  Weather  Service  (NWS),  who  work  in  the  federal  government;  and  

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those  in  the  academic  sector,  including  universities  and  professional  societies,  who  

contribute  scientific  research  and  investigate  operational  issues.2    

  A  few  months  after  the  chase  ended,  I  would  interview  the  Meteorologist  in  Charge  

of  forecasting  operations  for  the  NWS  office  in  Midland,  Texas.  He  had  issued  the  tornado  

alert  for  Saragosa  at  7:54  pm  on  May  22,  and,  the  next  day,  had  walked  through  the  

damaged  community  in  order  to  assess  the  storm’s  severity.3  During  our  conversation,  he  

placed  photos  of  the  destruction  on  the  table  in  front  of  me,  describing  as  he  did  the  

fragmented  buildings,  the  search  for  bodies  in  the  wreckage,  and  the  frantic  attempts  by  

local  meteorologists  to  understand  how,  in  spite  of  their  accurate  warnings,  this  town  had  

not  known  about  the  tornado  until  seconds  before  it  hit.  An  official  assessment  report  

conducted  by  the  NWS  concluded  of  this  event,  “If  the  ultimate  criterion  in  judging  the  

effectiveness  of  Saragosa’s  warning  system  is  whether  the  tornado  warning  reached  those  

at  risk,  it  must  be  concluded  that  the  warning  system  failed.”4    

  Retired  after  forty  years  as  a  meteorologist,  the  man  I  interviewed  that  day  pointed  

out  the  timeliness  of  the  tornado  warning,  noting  especially  the  unusually  long  “lead  time”  

given  for  people  in  Saragosa  to  take  cover.  “They  had  nearly  thirty  minutes  to  get  to  a  

shelter  before  the  tornado  hit  the  town.  Even  today,  that’s  unprecedented.”  A  few  years  

later,  in  1989,  he  was  awarded  the  American  Meteorological  Society’s  National  Exceptional  

Specific  Prediction  Award  for  his  role  in  warnings  issued  across  Texas  that  afternoon.  

However,  it  was  clear  to  me  that  he  found  this  accomplishment  difficult  to  marry  with  the  

images  of  destruction  he’d  witnessed  in  that  town,  and  perhaps  with  his  own  sense  of   2  American  Meteorological  Society,  “State  of  the  Weather  and  Climate  Enterprise.”  3  Layman,  Personal  Interview.  4  National  Research  Council,  “Saragosa,  Texas,  Tornado  May  22,  1987:  An  Evaluation  of  the  Warning  System,”  27.  

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responsibility.  What  haunted  him,  he  said,  was  the  knowledge  that  the  people  of  Saragosa  

didn’t  get  his  warning.  “They  should  have,”  he  said.  The  number  of  people  who  died  “on  his  

watch”—a  refrain  common  in  my  conversations  with  NWS  forecasters—troubled  him  still.  

“We  need  to  make  sure  this  doesn’t  happen  again.”  

  What  struck  me  then,  and  still  does  today,  is  the  great  effort  forecasters  commit  to  

getting  predictions  correct  and  how  this  determination  lives  alongside  the  concern  they  

express  for  their  various  publics  when  severe  weather  threatens.  In  part,  forecasters’  

interest  in  their  various  publics  comes  from  a  situatedness  of  place.  Forecasters  reside  in  

the  communities  they  serve,  attending  community  events,  school  functions,  or  church  with  

neighbors  who  frequently  ask  them  about  their  work,  the  latest  forecast,  an  upcoming  

storm.  Their  children  go  to  schools  and  their  spouses  work  in  businesses  that  are  

potentially  in  harm’s  way  in  bad  weather.  Their  concern  becomes  most  salient  when  

people’s  lives  are  at  stake,  which  can  occur  during  the  more  immediate  threats  of  

tornadoes  or  and  heavy  rain  or  the  gradual  dangers  of  hurricanes  or  snow.  This  immersion  

in  a  risk  society5  of  environmental  hazards  that  forecasters  help  adjudicate  and  generate  

means  that,  like  Beck’s  experts,  they  cannot  escape  the  responsibility  for,  nor  the  effects  of,  

their  expertise.  Understandably,  this  troubles  them.  

  Protection  of  their  respective  “public”  sits  at  the  heart  of  forecasters’  work,  

motivating  them  as  scientists  and  as  public  servants.  Yet,  notions  of  how  to  protect  and  

what  protection  means  are  changing  and  are  doing  so  in  ways  that  have  consequences  for  

their  agency  and  for  people  affected  by  weather  on  the  ground.  On  the  one  hand,  many  of  

the  activities,  practices,  and  trainings  that  forecasters  engage  in  arise  within  the  context  of  

5  Beck,  Risk  Society:  Towards  a  New  Modernity.  

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their  profession’s  commitment  to  predictive  precision.  Daily  forecast  technologies,  warning  

procedures,  damage  assessment  protocols,  communication  strategies—these  reflect  norms  

of  accuracy  and  efficiency.  Interestingly,  official  agency  discourses  convey  such  values  as  

mechanisms  for  safeguarding  their  publics.6  Accuracy  is  a  means  of  protection,  a  way  of  

preventing  harm.  Accuracy  imbricates  public  safety.  But  as  the  forecaster  I  interviewed  that  

day  lamented,  a  technoscientific  fix  is  usually  not  enough.  As  is  suggested  in  more  recent  

NWS  agency  documents  and  through  my  observations  of  forecasters  in  action,  protection  of  

life  may  be  more  successful  if  accuracy  is  co-­‐constituted  with  attention  to  relationships  

built  with  people  across  a  spectrum  of  public  safety  experts  and  with  different  lay  publics.  

The  ethical  dimensions  of  forecaster  knowledge  and  expertise  manifest  in  various  ways,  

predominantly  through  a  commitment  to  accuracy,  efficiency  and  the  like,  but  also,  I  

suggest,  to  care.    

  Over  the  last  few  decades,  social  scientists  from  disaster  and  hazards  disciplines  

have  increasingly  been  invited  to  join  the  Weather  Enterprise  in  the  common  goal  of  

improving  the  warning  system  to  minimize  loss  of  life  from  dangerous  weather.  Many  in  

this  community  characterize  partnerships  between  physical  and  social  scientists  such  that  

the  former  offer  only  insight  into  the  atmosphere  while  the  latter  offer  complementary  

insight  into  society.    I  see  my  role  as  one  who  complicates  these  framings,  showing  how  

conceptions  of  the  one—forecasters,  the  atmosphere,  and  “the  public”—are  intertwined  

with  the  other,  as  well  as  with  the  technologies  and  knowledge  production  of  prediction.  

For  example,  hazardous  weather  means  little  if  there  are  no  people  to  experience  it.  

Similarly,  forecasters  determine  hazards  based  on  both  atmospheric  criteria  instantiated  in  

6  Goodsell,  “U.S.  National  Weather  Service.”  

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their  technologies  and  their  “impacts,”  or  effects,  on  humans.  And  the  atmosphere  is  

conceptualized  through  predictive  apparatuses  as  weather  only  in  the  context  of  the  people  

who  name  and  live  it.  Such  mutual  constitutions  are  likewise  evident  in  how  forecasters  

and  publics  talk  about  one  other,  the  expectations  they  have  of  one  another,  and  their  

sociotechnical  imaginaries  of  what  they  and  the  other  ought  to  be.7    

  This  dissertation  attempts  to  answer  some  of  the  questions  I  had  that  day  in  

Saragosa—and  many  more  I’ve  wondered  about  since.    For  now,  I  focus  on  forecasters  and  

examine  key  norms  and  values  that  have  played  an  important  part  in  how  they  have  seen  

themselves  in  relationship  to  society  over  time  and  how  they  are  framing  this  connection  

for  the  future.  I  hope  to  offer  alternative  images  of  what  their  role  might  look  like  and  how  

examples  of  such  choices  already  exist  in  their  daily  work.  I  do  so  through  an  examination  

of  various  ethical  dimensions  of  forecasters’  practices,  including  the  historical  and  ongoing  

debates  about  their  proper  role  in  society  and  dominant  images  of  who  they  might  be  in  

relationship  to  those  people  they  serve.    In  this  sense,  my  work  is  normative  and  as  such,  

prescriptive.  Such  an  analysis,  I  hope,  finds  meaning  among  my  colleagues  in  the  Weather  

Enterprise  and  Science  and  Technology  Studies  disaster  scholars,  and  suggests  possible  

changes  in  operational  meteorology  that  may  lead  toward  more  positive  and  just  futures.    

7  Jasanoff,  “Future  Imperfect:  Science,  Technology,  and  the  Imaginations  of  Modernity.”  

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Introduction           “Oh,  would  some  power  the  gift  give  us/  To  see  ourselves  as  others  see  us!?”       -­‐-­‐Robert  Burns,  “To  a  Louse,  On  Seeing  One  on  a  Lady's  Bonnet  at  Church”  1786    

  Weather  prediction  is  an  ethically  complex  profession  in  which  scientific  experts  

create  forecasts  and  issue  alerts  that  have  direct  effects  on  public  safety.  Forecasters  

employed  either  publicly  in  government  agencies  or  privately  in  various  industries  share  

the  common  goal  of  accurately  and  quickly  predicting  weather  phenomena  that  threaten  

lives.  In  this  sense,  their  work  is  high  risk,  as  it  can  result  in  material  consequences  for  

members  of  their  publics,  including  the  possibility  of  death  or  injury.  This  effort  is  

conceptualized  as  one  based  in  responsibility,  a  professional  and  personal  ethic  that  

emerges  from  their  relationship  with  those  they  call  “customers”  or  “users.”8  A  guiding  

principle  of  weather  prediction,  then,  is  accountability  for  forecasts  of  future  atmospheric  

conditions  and  an  obligation  to  issue  them  in  a  timely  manner  such  that  individuals  can  

make  decisions  to  keep  themselves  and  their  loved  ones  safe.      

  In  the  discourse  of  weather  prediction,  tension  emerges  between  forecasters’  

dedication  to  predictive  accuracy  (e.g.  their  “passion”  for  their  science)  and  concern  over  

public  welfare  (e.g.  their  “service”  to  their  communities)  as  they  consider  their  own  role  in  

society.  In  their  daily  forecasting  work,  they  spend  their  days  characterizing  the  

atmosphere  by  assimilating  output  from  computer  models  and  observational  

instrumentation  with  their  knowledge  of  meteorology,  personal  experience  in  an  

operational  setting,  and  familiarity  with  local  weather.  That  is,  they  see  their  role  in  society  

8  “Weather  Ready  Nation:  NOAA’s  National  Weather  Service  Strategic  Plan.”  

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as  scientific  practitioners  skilled  in  the  science  of  forecasting.  When  dangerous  weather  

threatens,  they  draw  on  this  expertise  under  circumstances  of  high  uncertainty  to  make  

decisions  about  which  populations  sit  in  the  path  of  potential  severe  weather.  Here  they  

see  their  role  as  multiple.  They  function,  as  what  I  call  “managers  of  risk,”  or  those  who  

adjudicate  immediate  threats  of  dangerous  weather;  “watchful  guardians”9  of  their  publics,  

which  reflects  the  agency  mission  to  “protect  lives  and  property;”10  and  epistemic  

authorities  within  a  local  network  of  public  safety  officials  in  their  role  as  scientific  experts.  

Such  tension  is  especially  visible  among  those  employed  in  the  National  Weather  Service,  

where  weather  alerts,  called  “warnings,”  originate.11  

  National  Weather  Service  warnings  and  their  attending  ethical  dimensions  are  

unique  in  the  context  of  prediction  and  deserve  special  attention.  Issued  for  weather  

phenomena  classified  as  “severe”  or  “extreme,”  forecasters  adjudicate  which  types  of  

threats  meet  criteria  for  severe,  both  collectively  within  the  forecast  office  and  individually  

at  their  workstations.  Decisions  over  whether  and  when  to  warn  their  publics  may  be  

partially  made  through  consultation  with  other  forecasters  and  public  safety  officials;  

however,  attribution  of  “successful”  predictions  and  blame  for  “bad”  ones  falls  squarely  on  

the  forecasters  and  their  agency.  For  example,  each  year  stories  circulate  in  the  media  

about  how  people  who  survive  severe  weather  didn’t  know  it  was  coming.  Headlines  

announcing  storms  came  “without  warning”  suggest  that  forecasters  neglected  to  do  their  

jobs,  or  did  so  inadequately.  Interviews  with  individuals  who  claim  a  lack  of  notice  suggest  

9  Goodsell,  “U.S.  National  Weather  Service,”  79.  10  National  Oceanic  and  Atmospheric  Administration,  “NOAA’s  National  Weather  Service.”  11  National  Weather  Service  Act  of  1978.  

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to  scholars  that  warnings  only  count  if  they  come  through  mechanisms  and  with  advice  

that  people  can  access  and  act  on.12    

  Such  epistemological  claims  reveal  the  ways  that  warnings  function  as  boundary  

objects  between  different  groups  and  the  multiplicity  of  valences  for  what  gets  classified  as  

a  warning.13  In  recent  years,  forecasters  have  been  asked  to  expand  their  knowledge  and  

responsibility  in  warning  operations  beyond  the  creation  and  dissemination  of  their  

warning  as  a  “product.”  They  are  called  upon  by  their  own  agency  to  be  increasingly  

responsible  for  developing  “deep  relationships”  with  partners  to  understand  their  needs  

and  learning  about  methods  for  successful  communication  of  information  to  different  kinds  

of  publics.14  That  is,  forecasters  must  understand  the  variety  of  ways  warnings  are  

conceptualized,  defined,  understood,  and  counted.  And  they  must  contextualize  and  situate  

these  meanings  within  the  sphere  of  individual  users’  needs  and  decision  thresholds.  This  

work  is  not  necessarily  new  for  forecasters  who  have,  for  decades,  worked  with  partners  to  

improve  their  advice.  The  agency’s  framing  of  this  effort,  however,  is  new  in  what  it  

suggests  about  the  future  forecaster.  As  NWS  forecasters  move  towards  what  they  have  

begun  to  call  impact-­‐based  decision  support  services,  or  “interpretive,”  activities  that  

extend  and  challenge  common  classifications  of  warnings,  they  have  also  begun  to  question  

this  new  role  of  what  forecasters  ought  to  be.    

12  Morss,  Demuth,  and  Lazo,  “Communicating  Uncertainty  in  Weather  Forecasts:  A  Survey  of  the  U.S.  Public.”;  Morss  et  al.,  “Improving  Societal  Outcomes  of  Extreme  Weather  in  a  Changing  Climate:  An  Integrated  Perspective”;  Lazo,  Morss,  and  Demuth,  “300  Billion  Served:  Sources,  Perceptions,  Uses,  and  Values  of  Weather  Forecasts”;  Schumacher,  “Multidisciplinary  Analysis  of  an  Unusual  Tornado:  Meteorology,  Climatology,  and  the  Communication  and  Interpretation  of  Warnings,”  2010.  13  Star  and  Griesemer,  “Institutional  Ecology,  ‘Translations’  and  Boundary  Objects:  Amateurs  and  Professionals  in  Berkeley’s  Museum  of  Vertebrate  Zoology,  1907-­‐39.”  14  Swanson-­‐Kagan  et  al.,  “Update  on  the  NWS  Operations  and  Workforce  Analysis.”  

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  In  the  high  stakes  scenarios  of  warnings,  where  risk  is  immediate  and  outcomes  

uncertain,  frictions  arise  as  forecasters  make  decisions  about  what  information  to  share,  

how  to  narrate  and  display  predictions,  what  types  of  alerts  to  issue,  and  why.  Conflicts  

likewise  materialize  based  around  differences  in  individual  and  institutional  norms,  

forecaster  experience  and  skill,  and  practices  specific  to  forecasters’  respective  local  office  

culture.  Attending  to  these  tensions  are  beliefs  about  the  purpose  of  forecasters,  about  “the  

public,”  and  about  the  future  of  their  enterprise.  Together,  these  constitute  the  

sociotechnical  assemblages  of  predictive  work,  which  perform  and  create  complex  ethical  

dimensions  for  forecasters.  .    

  This  dissertation  examines  the  weather  warning  process  in  the  National  Weather  

Service  through  important,  though  often  unexamined,  ethical  dimensions  that  arise  in  

forecaster  work  and  agency  goals.  I  argue  that  the  agency  emphasizes  accuracy  and  

timeliness  at  the  expense  of  other  values,  which  leaves  forecasters  unclear  about  several  

aspects  of  their  professional  identities.  This  is  not  to  say  that  accuracy  is  not  important.  It  

is.  Success  could  not  be  had  without  it.  Yet  viewing  their  science  through  a  dominant  ethic  

of  accuracy  calls  into  question  their  role  in  society  in  its  exclusive  focus  on  them  as  

predictors  of  daily  weather,  a  skill  that  is  increasingly  troubled  as  computer  models  

improve.  Competition  with  their  machines  affects  those  strategies  forecasters  use  to  

maintain  their  scientific  authority  as  their  work  moves  away  from  daily  prediction  and  

toward  an  emphasis  on  relationships.  And  it  shapes  their  efforts  to  adjudicate  institutional  

norms  that  support  an  ethic  of  accuracy  with  those  of  care  and  concern  for  their  

communities.  An  examination  of  these  tensions  is  important  to  identifying  those  non-­‐

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dominant  ethics  that  exist  in  their  work  and  to  providing  alternative  images  of  what  it  

means  to  be  a  weather  forecaster  in  service  to  society.    

  I  propose  that  the  National  Weather  Service  and  its  meteorologists  should  rethink  

forecasting  science  through  their  relationship  to  multiple  publics,  making  visible  in  their  

trainings,  practices,  and  identities  both  the  precision  and  care  that  already  attend  to  their  

work.  I  offer  the  concept  of  “empathetic  accuracy”  as  an  alternative  ethic  that  allows  

forecasters  to  focus  on  predictive  precision  through  their  commitment  to  a  relational  ethic  

with  their  publics.  While  forecasters  might  read  this  as  a  suggestion  to  include  concerns  

outside  the  scope  of  their  expertise  and  knowledge  as  scientists,  I  would  suggest  they  

already  exist  as  a  crucial  part  of  both.  By  making  empathetic  accuracy  visible  in  their  

identities  and  thus  their  profession,  forecasters  and  administrators  can  better  meet  their  

goals  to  create  an  agency—and  a  personal  predictive  practice—more  responsive  to  

people’s  situated  lives.  In  this  sense,  I  ask  questions  about  what  counts  as  forecasting  

knowledge  and  who  decides.15  

  I  selected  an  emphasis  on  ethics  for  four  reasons.  First,  the  NWS  mission  is  

expressed  as  an  ethical  commitment  to  protecting  their  publics  from  harm.  This  creates  an  

opportunity  for  analysts  to  explore  what  such  an  obligation  affords  in  terms  of  the  agency’s  

technoscientific  developments  and  practices,  what  responsibilities  it  creates  between  

forecasters  and  relevant  individuals  or  groups,  and  how  it  gets  expressed  in  agency  policies  

and  procedures.    An  analytically  rich  and  little  understood  arena,  investigating  the  ethical  

dimensions  of  weather  warnings  offers  me,  the  analyst,  the  chance  to  use  “ethic”  as  a  

language  of  visibility,  revealing  norms  and  values  that  are  difficult  to  discern  within  the   15  Haraway,  “Situated  Knowledges:  The  Science  Question  in  Feminism  and  the  Privilege  of  Partial  Perspective.”  

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sciences  of  operational  meteorology  and  their  attending  sociotechnical  and  political  

apparatuses.  

  Second,  I  share  their  commitment  to  protecting  lives.  I  have  participated  in  this  

community  for  nearly  five  years  as  a  social  scientist,  a  designation  forecasters  give  to  those  

who  study  the  human  dimensions  of  weather  extremes.  While  the  term  social  scientist  may  

be  an  imperfect  universal  label  for  all  who  work  in  social,  cultural,  and  human  concerns  of  

weather  disasters—there  is  no  comparable  label  for  those  doing  humanistic  work,  for  

example—it  is  the  label  forecasters  are  familiar  with  and  the  one  they  have  given  me.  Thus  

it  is  the  one  I  choose  to  perform.    

  In  this  role,  forecasters  and  agency  officials  have  turned  to  us  with  requests  for  help  

in  revealing  and  addressing  problems  in  the  warning  process,  though  often  they  have  in  

mind  an  agenda  aimed  toward  understanding  “the  public”  and  not  necessarily  themselves.  

My  critical  participation16  has  created  for  me  an  ethical  obligation  to  respond.  Thus,  I  have  

spent  the  last  five  years  “studying  up”17  within  their  institution  to  identify  places  of  

possible  intervention—and  one  place  is  ethics.  An  emphasis  on  ethics  allows  me  to  push  

forecasters’  concerns  into  places  where  I  think  they  ought  to  be,  in  this  case,  toward  an  

examination  of  the  values  operating  in  the  domain  of  “the  expert”  forecaster,  which  is  what  

I  explore  in  articles  one  and  two.  Encouraging  a  symmetrical  analysis  of  the  warning  

process  from  both  expert  and  public  points  of  view  becomes  part  of  my  fulfillment  of  the  

analyst’s  ethical  obligation.  

16  Downey,  “What  Is  Engineering  Studies  For?  Dominant  Practices  and  Scalable  Scholarship,”  2009.  17  Nadar,  “Up  the  Anthropologist:  Perspectives  Gained  from  Studying  up”;  Priyadharshini,  “Coming  Unstuck:  Thinking  Otherwise  about  ‘Studying  Up.’”  

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  Third,  as  an  STS  scholar,  an  emphasis  on  ethics  is  one  important  and  common  

disciplinarily  way  for  me  to  participate.  Understanding  what  forecasters  value  in  their  

knowledge  production  and  practices  reveals  how  they  produce  a  certain  vision  of  society.  If  

it  is  one  that  is  “resilient”  against  severe  weather  and  the  multiplicity  of  disasters  that  

might  delay  such  ability,  then  someone  needs  to  help  unpack  what  inflections  resilience  

might  carry  and  for  whom  and  at  what  cost,  as  I  do  in  article  three.  Related  to  this  is  how  

these  values  in  society  connect  to  those  in  their  agency,  through  what  mechanisms,  

discourses,  and  activities?  Ought  these  be  the  values  forecasters  should  expect  of  their  

publics?  Of  their  own  profession?  How  ought  lay  people  factor  into  decisions  about  

weather  warnings  and  technological  development?  How  do  they  already?  In  short,  I  want  

to  be  well  prepared  to  “fill  the  ethics  seat”  in  weather  disaster  contexts,  as  many  in  STS  

have  suggested  we  might  be  called  to  do.18    My  expertise,  then,  lies  in  offering  an  

ethnographic,  historical,  and  discursive  exploration  of  ethics  that  have  shaped  and  been  

shaped  by  NWS  sociotechnical  developments,  as  well  as  those  concerns  frequently  raised  

by  forecasters  in  forecast  offices,  professional  society  meetings,  and  research  agenda.  

  Finally,  this  dissertation  reflects  my  critical  participation  as  a  Disaster  STS  scholar.  

Working  out  my  understanding  of  important  norms  and  values  for  forecasters,  I  believe,  

underscores  the  embodied  nature  of  my  own  work,  which  I  address  in  article  four.  It  

highlights  what  Cohen  and  Galusky  suggest  is  “the  lived  cultural  and  personal  experiences  

of  scholars  [like  myself]  and  [our]  scholarship  within  larger  sociotechnical  systems.”19  As  a  

18  Fisher,  “Ethnographic  Invention:  Probing  the  Capacity  of  Laboratory  Decisions”;  Fisher,  “Public  Science  and  Technology  Scholars:  Engaging  Whom?”;  Woolgar,  Coopmans,  and  Neyland,  “Does  STS  Mean  Business?”;  Schuurbiers,  “What  Happens  If  the  Lab  Does  Not  Stay  in  the  Lab?:  Applying  Midstream  Modulation  to  Enhance  Reflection  in  the  Laboratory.”  19  Cohen  and  Galusky,  “Guest  Editorial,”  3.  

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friend  and  colleague  to  many  forecasters,  I  employ  a  relational  ethic  with  those  I  study,  one  

that,  as  Carolyn  Ellis  has  written,  “recognizes  and  values  mutual  respect,  dignity,  and  

connectedness  between  researcher  and  researched,  and  between  researchers  and  the  

communities  in  which  they  live  and  work.”20  It  is  a  difficult  ethic  to  navigate  at  times  as  I  

listen,  learn,  and  critique.  But  it  is  worth  the  effort.  

 

Site  Relevance  &  Methods  

  The  National  Weather  Service  is  the  government  agency  mandated  by  Congress  

since  1870  to  collect  and  assess  information  about  the  atmosphere  and  from  this  “collage”  

of  “highly  complex  information”21  to  construct  and  communicate  daily  forecasts.  Likewise,  

forecasters  employed  in  one  of  122  weather  forecast  offices  (WFOs)  across  the  country  

have  been  given  sole  responsibility  for  issuing  warnings  to  the  general  public  during  severe  

weather.  In  part,  the  NWS  mission,  as  stated  in  strategic  plans,  is  to  “protect  lives  and  

property,”22  a  goal  that  forecasters  embody  in  multiple  ways  throughout  their  operational  

work.  Warnings  inflect  this  mission  as  an  important  part  of  the  larger  sociotechnical  

infrastructure  that  arranges  their  daily  labor,  their  practices,  and  their  relationships.  

Imbricated  within  this  scientific  enterprise  are  the  political,  ethical,  and  social  dimensions  

of  prediction,  which  likewise  affect  how  forecasters  see  themselves  relative  to  their  

profession  and  their  publics.  The  National  Weather  Service  and  its  forecasters  sit  at  the  

20  Ellis,  “Telling  Secrets,  Revealing  Lives:  Relational  Ethics  in  Research  with  Intimate  Others,”  4.  21  Daipha,  “Weathering  Risk:  Uncertainty,  Weather  Forecasting,  and  Expertise.”  22  “Weather  Ready  Nation:  NOAA’s  National  Weather  Service  Strategic  Plan.”  

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center  of  predictive  controversies  in  their  role  as  the  originators  of  warnings,  or  the  

“wholesaler  of  weather  information,”  as  Daipha  puts  it.23  

  Ethnographic  Work  

  I  began  my  observations  of  the  National  Weather  Service  forecasting  and  warning  

process  within  their  workspace,  or  Weather  Forecast  Offices  (WFO),  in  2012.  From  October  

2012  to  July  2013,  I  watched  forecasters  in  an  office  in  the  mid-­‐Atlantic  work  once  a  week  

for  four  hours,  learning  the  jargon,  technologies,  and  practices  of  forecasting.  I  kept  427  

pages  of  field  notes  and  audio  recorded  50  hours  of  conversations  and  daily  work  with  

meteorologists.  From  2014  to  2016,  I  spent  nine  months  in  two  other  forecast  offices,  one  

in  the  West  (8  months)  and  the  other  in  the  Southeast  (1  month).  In  the  former,  I  sat  with  

forecasters  from  January  2014  to  August  of  2016.  For  the  first  three  months,  I  arrived  at  

the  office  for  six-­‐hour  visits  twice  a  week  to  acclimate  to  the  particulars  of  this  office’s  

approach  to  forecasting  and  warnings.  Beginning  in  March,  I  followed  the  weather,  arriving  

on  days  leading  up  to  anticipated  tornadoes  or  flash  flooding,  stayed  throughout  the  

warning  process,  and  joined  forecasters  for  any  damage  surveys  or  post-­‐event  debriefings;  

I  interviewed  22  meteorologists,  including  those  who  work  on  their  technologies.  In  the  

latter,  I  spent  2  weeks  specifically  watching  forecasters  discuss  and  issue  warnings  on  

overlapping  threats  of  tornado  and  flash  flood,  and  discussing  the  technologies  and  

strategies  used  to  create  them.  I  interviewed  11  meteorologists  over  the  course  of  another  

week.  All  told,  I  spent  93  days  in  these  two  offices,  audio  recording  all  interactions  (529  

hours).    

  Further  I  have  been  critically  participating  as  a  presenter  and  organizer  of  annual  

23  Daipha,  Masters  of  Uncertainty:  Weather  Forecasters  and  the  Quest  for  Ground  Truth,  28.  

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conference  sessions  attended  by  NWS  meteorologists  and  many  others  in  the  Weather  

Enterprise.  My  participation  in  these  meetings  not  only  helps  me  better  understand  

forecasters  but  how  they  fit  into  the  larger  weather  community  and  interact  with  different  

groups.    For  the  past  two  years,  I  have  served  as  the  chair  of  the  Societal  Impacts  

Committee  for  the  National  Weather  Association  and  have  presented  at  and  won  awards  

for  the  presentation  of  my  work.    I  am  currently  on  the  American  Meteorological  Society’s  

Board  of  Societal  Impacts  and  a  member  of  a  special  conference  planning  committee  for  a  

symposium  to  be  held  in  2017  called  “Special  Symposium  on  Individual,  Social,  and  Cultural  

Observations  in  Weather  and  Climate  Contexts.”    

  As  part  of  the  social  sciences  community  for  these  two  conferences,  I’ve  helped  

shape  agenda  and  speaker  choices,  specifically  becoming  known  in  these  circles  as  

someone  who  can  speak  to  ethical  dimensions  of  warnings  and  as  someone  who  researches  

NWS  forecaster  challenges  and  technologies.  More  importantly,  becoming  an  integrated  

member  of  the  community  keeps  me  abreast  of  issues  important  to  forecasters  and  those  

that  I  believe  ought  to  be.  For  example,  I’ve  given  two  conference  presentations  on  the  

value  of  building  relationships  through  the  Integrated  Warning  Team  initiative,  a  

grassroots  assemblage  of  people  from  the  weather  community  in  a  local  area.  I’ve  argued  

that  there  are  other  key  groups  missing  from  these  meetings,  including  those  not  

traditionally  seen  as  partners  in  the  Weather  Enterprise:  storm  chasers,  storm  spotters,  

military  facilities,  administrators  of  nursing  and  retirement  homes,  representatives  from  

the  general  public,  and  those  who  direct  faith  based  and  community  organizations.    

  Finally,  last  year  I  was  invited  to  join  in  an  NWS  initiative  called  Impact-­‐Based  

Decision  Support  Services  (IDSS),  by  helping  facilitate  and  organize  a  yearlong  series  of  

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webinars.  To  date,  there  have  been  eight  webinars.  Agency  administrators  are  developing  

ideas  for  what  will  count  as  IDSS  in  light  of  an  agency  restructuring  of  NWS  staff  and  

reexamining  the  function  of  the  agency.  Amid  Congressional  and  private  sector  criticisms  

about  the  budget  and  the  need  for  the  organization  to  maintain  a  structure  that  has  been  in  

place  since  the  1980s,  the  agency  hired  a  consulting  firm,  McKinsey  &  Company,  to  help  

administrators  develop  a  new  plan  that  “evolves  the  culture”  of  the  NWS.  As  I  discuss  in  

article  three,  IDSS  is  one  of  the  important  initiatives  gaining  support  in  the  new  philosophy  

of  the  agency,  which  focuses  on  “deep  relationship”  with  core  partners.24  Participating  in  

this  series  of  webinars  has  given  me  insight  into  where  the  operational  forecasters  see  

themselves  and  how  they’re  interpreting  the  initiative,  and  creating  IDSS  activities  in  light  

of  their  daily  work.  

  Archival  and  Historical  Work  

  I  have  drawn  from  online  historical  archives  of  scholarly  journals,  monthly  

newsletters,  and  conference  proceedings  in  the  meteorological  community.  I  also  spent  

three  weeks  at  the  NOAA  library  at  the  National  Center  for  Atmospheric  Research  in  

Boulder  where  I  photocopied  235  pages  of  memoranda,  technical  manuals,  and  conference  

preprints  about  NWS  warning  technologies.  Further,  I’ve  collected  materials  from  private  

archives  in  individual  offices  at  the  Global  Systems  Division  in  the  NOAA  Earth  Systems  

Research  Lab,  also  in  Boulder,  including  two  training  manuals  about  warning  software.  

Aside  from  interviews  and  observations,  this  material  is  the  best  source  for  understanding  

how  forecasters  have  identified  challenges  related  to  their  profession  and  their  role  in  

society,  as  well  as  the  assemblages  of  sociotechnical,  ethical,  and  political  discourse  of  

24  Swanson-­‐Kagan  et  al.,  “Update  on  the  NWS  Operations  and  Workforce  Analysis.”  

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forecasting  from  the  1970s  to  today.  From  these  visits,  I  have  collected  3,372  pages  of  hard  

copy  reproductions  and  another  347  digital  pages  of  material  related  to  forecasting,  

weather  prediction,  and  sociotechnical  developments.    

  While  not  all  of  the  material  I’ve  collected  from  archives  or  obtained  from  

ethnographic  work  over  the  past  four  years  is  explicitly  represented  in  this  dissertation—

scholars  tend  to  collect  much  more  than  we  can  use  in  any  one  project—it  informs  my  

work  in  tacit  ways.    Continual  exposure  to  this  community,  and  my  participation  within  it,  

complements  this  material  such  that  I  feel  I  know  NWS  forecasters  as  an  interactional  

expert.25  As  evidence  of  this  role,  I’m  frequently  called  on  to  represent  their  point  of  view  in  

social  science  circles  within  the  Weather  Enterprise,  and  though  there  are  many  problems  

with  representation,  forecasters  themselves  have  nominated  me  to  speak  on  their  behalf  

and  have  invited  me  into  their  offices.  Their  generosity  and  trust  humbles  me.      

Terms  and  Literatures  

  Because  this  dissertation  takes  the  form  of  a  series  of  manuscripts,  detailed  

literature  reviews  are  included  in  each  article  and  tailored  to  their  specific  purposes.  Other  

literatures  relevant  to  the  form  or  purpose  of  the  articles  can  be  found  in  the  commentary  

prior  to  each  article,  when  necessary.  Still,  here  I  offer  a  few  notes  about  overarching  

connections  to  STS  literatures  that  shape  my  assumptions  and  approaches  to  my  

ethnographic  and  historical  work.      

  By  emphasizing  an  “ethic  of  ”  particular  norms  and  values  emerging  within  the  NWS  

warning  process,  I  wish  to  distinguish  my  use  of  the  term  from  that  of  Ethics,  the  larger  and  

systematic  philosophical  inquiry  into  virtues,  morals,  and  metaphysics  generally.  

25  Collins,  Evans,  and  Gorman,  “Trading  Zones  and  Interactional  Expertise.”  

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Specifically,  I  am  looking  at  particular  values,  or  ethos,  that  arise  within  situated  

circumstances  of  weather  warnings  and  in  the  discourse  of  forecasting  as  a  profession—an  

applied  ethics  of  weather  prediction,  so  to  speak.  Each  ethic  I  call  out,  then,  is  a  localized  

and  contextual  normative  commitment  or  value  that  co-­‐exists  with  other  institutional  and  

personal  norms,  sociotechnical  infrastructures,  and  practices  and  policies  within  the  

National  Weather  Service.    

  Other  terms  merit  clarification.  Weather  prediction  in  this  dissertation  subsumes  

both  forecasting  and  warning  practices,  reflecting  the  fluidity  of  forecasters’  work  in  both  

arenas  of  expertise.  And  I  explore  such  terms  through  practices  and  assumptions  made  by  

the  National  Weather  Service,  my  site  for  this  study.  Forecasting,  as  other  scholars  have  

shown,  consumes  the  bulk  of  their  predictive  activities.26  Warnings  are  a  special  kind  of  

activity,  one  that  occurs  more  infrequently  in  many  offices  and  requires  different  software,  

skillsets,  and  experiences  to  do  well.    

  Weather  and  weather  events  are  likewise  terms  that  merit  closer  critical  attention,  a  

task  that  is  outside  the  scope  of  this  dissertation.  I  use  them  in  the  dissertation  as  

forecasters  might:  weather  is  the  interaction  of  atmospheric  processes  that  create  

phenomena,  such  as  clouds,  precipitation,  or  storms.  Another  common  use  is  as  a  reference  

for  a  lack  of  threatening  or  dangerous  weather,  as  in  “No  weather”  or  “fair  weather.”  Severe  

weather,  in  contrast,  is  the  kind  of  atmospheric  conditions  that  have  the  potential  to  affect  

people  on  the  ground  in  negative  ways.  Weather  events  are  singular  occurrences  of  

weather  or  related  occurrences  that  happen  in  a  short  period  of  time.  They  are  bound  by  

spatial  and  temporal  concerns.  An  event  might  be  a  solitary  storm,  like  a  hurricane  or   26  Fine,  Authors  of  the  Storm:  Meteorologists  and  the  Culture  of  Prediction;  Daipha,  “From  Bricolage  to  Collage:  The  Making  of  Decisions  at  a  Weather  Forecast  Office.”  

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tornado,  for  example,  or  a  cluster  of  storms  that  move  together  across  the  country  over  24  

hours,  like  a  blizzard  or  “outbreak”  of  tornadoes.    

  Finally,  what  counts  as  a  forecaster  is  not  always  easy  to  determine.  The  American  

Meteorological  Society  and  the  National  Weather  Association  offer  multiple  credentials  and  

educational  criteria27  that  must  be  met  to  practice  forecasting  and  to  do  so  in  a  manner  

recognized  as  ethical  by  their  profession.28  For  example,  an  individual  on  social  media  

might  make  a  prediction  about  a  particular  weather  event  but  to  attain  the  expert  label  of  

forecaster,  they  have  to  perform  the  educational  and  social  aspects  of  the  profession.  For  

this  dissertation,  I  am  focusing  on  individuals  who  work  in  the  specific  role  as  forecaster  

within  the  National  Weather  Service  and  assume  that  staff  hired  to  work  in  this  agency  

meet  these  educational  or  equivalent  requirements,  though  training  on  the  specific  

sociotechnical  aspects  of  forecasting  practice,  as  well  as  their  personal  skill,  can  be  

localized  and  idiosyncratic.    

  Ethics  of  Warnings  Literature  

  In  focusing  on  the  ethical  dimensions  of  weather  prediction,  my  work  is  in  

conversation  with  several  scholars  in  Science  and  Technology  Studies.  First,  I  find  common  

ground  with  those  sociologists  of  science  who  have  explored  norms  common  to  science  and  

technology  itself  and  to  specific  instances  of  knowledge  production.  For  example,  while  I  

am  not  debating  the  norms  and  ideologies  of  science,  as  Merton,  Mitroff,  and  Mulkay  did,  I  

27  American  Meteorological  Society,  “The  Bachelor’s  Degree  in  Meteorology  or  Atmospheric  Science”;  American  Meteorological  Society,  “What  Is  a  Meteorologist?  A  Professional  Guideline.”  28  Hill  and  Mulvey,  “Business  Ethics  for  Professional  Meteorology:  Expectation  and  Satisfied  Customers”;  Hill  and  Mulvey,  “The  Ethics  of  Defining  a  Professional:  Who  Is  a  Meteorologist?”;  Meisner,  Hill,  and  Mulvey,  “Ethics  for  Government  Meteorologists”;  Hill  and  Mulvey,  “Resources  and  Guidance  for  Ethics  and  Personal  Conduct  in  Meteorology.”  

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agree  with  Merton  that  norms  may  be  “legitimatized  in  terms  of  institutional  values”  and  

“are  in  varying  degrees  internalized  by  the  scientist”  in  ways  that  shape  and  are  shaped  by  

their  practices  and  activities.29    

  In  the  case  of  the  forecasters,  values  of  accuracy  as  projected  through  the  image  of  

“the  man-­‐machine  mix,”  which  I  discuss  in  the  first  article,  may  indeed  create  a  “story  book  

image  of  [forecasting]  science,”  an  ideal,  that  deviates  from  their  actions.30  And  like  Mitroff,  

I  see  not  only  dominant  values  operating  in  forecasting,  like  accuracy,  but,  at  the  same  time,  

their  counter.31  In  the  second  article,  for  example,  I  aim  to  juxtapose  the  more  objective  

nature  of  accuracy  explored  in  article  one  with  a  very  intimate  accounting  of  an  ethic  of  

care  in  action.  I  suggest  these  two  are  less  contrasting  values—accuracy  and  care—as  

complementary,  and  in  the  context  of  protecting  lives,  essential  to  one  another.  Finally,  I  

see  the  science  of  forecasting  as  moving  toward  an  imperative  implicated  in  their  own  

sense  of  responsibility  for  their  communities’  safety.  In  article  three,  I  suggest  this  is  seen  

most  clearly  through  an  ethic  of  resilience,  one  that  is  as  much  about  their  own  survival  as  

a  profession  as  about  those  they  protect.  As  Shapin  and  Shafer  point  out,  "Knowledge,  as  

much  as  the  state,  is  the  product  of  human  actions.”32  We  are  also,  then,  responsible  for  

what  we  know.  

  These  scholars  are  part  of  a  tradition  paralleled  by  many  in  the  philosophy  of  

science  and  technology  that  calls  attention  to  the  social  contexts  and  problems  of  scientific  

29  Merton,  “The  Normative  Structure  of  Science,”  269.  30  Mulkay,  “Norms  and  Ideology  in  Science,”  647.  31  Mitroff,  “Norms  and  Counter-­‐Norms  in  a  Select  Group  of  the  Apollo  Moon  Scientists:  A  Case  Study  of  the  Ambivalence  of  Scientists.”  32  Shapin  and  Schaffer,  Leviathan  and  the  Air-­‐Pump:  Hobbes,  Boyle  and  the  Experimental  Life,  344.  

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and  technological  development.33  For  example,  I  explore  the  ethical  dimensions  of  

meteorological  science  that  get  folded  into  practice  and  outcomes  in  order  to  begin  a  

conversation  about  how  and  whether  we  ought  to  improve  this  science.  That  is,  similar  to  

scholars  like  Longino,  I  believe  we  ought  to  include  in  our  analysis  attention  to  the  “roles  

and  values,  interests,  and  relationships  in  the  social  and  cultural  context  of  science  that  

play  in  scientific  judgment”  and  the  impact  of  “science  and  science-­‐based  technologies”  on  

society.34  I  look  explicitly  at  the  shift  in  values  and  interests  in  the  National  Weather  

Service  and  argue  that  attention  to  the  ethic  of  resilience  through  care  and  accuracy  opens  

up  an  opportunity  to  better  understand,  and  perhaps  improve,  forecasters  relationship  

with  their  communities.      

  Focusing  on  the  warning  process  aligns  me  with  those,  such  as  Winner  and  Jonas,  

who  ask  questions  about  the  politics  and  power  of  technology.  Within  the  ethical  frame  of  

“objects”  and  “dynamics”  of  technology,  one  might  ask,  “What  are  the  chances  and  what  are  

the  means  of  gaining  control  of  the  process  [of  technological  invention]  so  that  the  results  

of  any  ethical  …  insights  can  be  translated  into  effective  action?”35  I  am  less  concerned  

about  questions  over  control  of  technology  processes  in  meteorology,  per  se,  than  about  

posing  questions  regarding  expert  assumptions  of  their  role  in  society  before,  during,  and  

after  technological  developments  occur.  Thus,  my  normative  undertaking  suggests  action  

and  thus  mirrors  advice  that  some  philosophers  give  with  regard  to  the  technoscientific  

enterprises  in  which  they  choose  to  engage.  Additionally,  like  Fuller,36  I  ask  about  how  we  

in  STS  are  to  make  decisions  about  the  participation  of  different  groups  in  the  directions   33  Ravetz,  Scientific  Knowledge  and  Its  Social  Problems.  34  Longino,  “How  Values  Can  Be  Good  for  Science,”  127.  35  Jonas,  “Toward  a  Philosophy  of  Technology,”  41.  36  Fuller,  “The  Future  of  Science  and  Technology  Studies.”  

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and  governance  of  society—that  is,  for  any  knowledge  problem,  how  do  we  move  (and  

when  and  why)  from  is  to  ought?37    Not  only  is  this  question  present  in  the  first  three  

articles  which  explore  the  content  of  is  and  ought  in  the  processes  of  knowledge  production  

in  the  forecasting  community  but  in  my  role  as  a  disaster  scholar.  Article  four  takes  up  this  

question  most  directly,  suggesting  that  there  is  no  easy  answer  given  the  state  of  

compromise  that  attends  to  such  questions.  However,  an  ethic  of  relationality  offers  the  

STS  scholar  one  potential  guide  in  moving  forward  with  suggestions.      

  I  make  no  claims  to  being  a  philosopher  or  to  having  been  trained  as  an  ethicist  in  a  

formal  way.  Instead,  I’m  philosophically  inclined  toward  normativity  because  forecasters  

have  asked  for  my  help  and  I  feel  that  acceptance  into  their  community  compels  me  not  

only  to  address  concerns  they  have  but  also  to  note  when  I  see  issues  that  ought  to  concern  

them.  I  have  spent  significant  time  with  them,  sitting  with  them  for  nearly  two  years,  and  as  

such  have  a  unique  understanding  of  their  work  that  allows  me  to  make  such  judgments.  I  

do  so  with  caution  and  with  the  knowledge  that  I  am  only  a  small  part  of  a  much  bigger  

assemblage  of  policies,  practices,  and  interests  that  shape  change.  Still,  I  take  my  work  

seriously,  just  as  forecasters—and  my  colleagues  in  the  Weather  Enterprise—do,  too.    

  I  wish  to  note  here  that  like  others  who  expose  the  complex  entanglements  to  which  

their  line  of  inquiry  points,  I  respect  the  expertise  and  dedication  of  those  that  I  participate  

with.38  Forecasters  take  seriously  their  science  and  their  hard  won  understanding  of  local  

weather  phenomena,  just  as  they  do  their  commitment  to  helping  people  stay  safe.  They  

know  the  atmosphere  well  and  are  skilled  at  their  craft.  Yet  the  intersectionality  of  many   37  Turner,  Explaining  the  Normative.  38  Lochlann,  Malignant:  How  Cancer  Becomes  Us;  Gusterson,  Nuclear  Rites:  A  Weapons  Laboratory  at  the  End  of  the  Cold  War.;  Traweek,  Beamtimes  and  Lifetimes:  The  World  of  High  Energy  Physics.  

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social,  political,  ethical,  legal,  and  public  aspects  of  their  work  and  its  affect  on  their  publics  

often  fall  outside  this  expertise,  or  their  ability  to  act  on  them—a  point  many  forecasters  

would  readily  acknowledge.  One  might  think  of  these  as  subsumed  under  the  label  of  non-­‐

meteorological  issues,  those  that  are  imbricated  with  their  knowledge  and  technologies,  of  

course,  but  in  many  ways  get  masked  by  the  daily  rhythms  of  weather  prediction.      

  As  predictive  experts  engaged  in  the  communication  of  weather  threats,  literatures  

that  theorize  the  cultural,  social,  and  constructed  nature  of  risk  also  find  relevance  here.  

The  projects  of  risk  assessments  and  risk  communication,  which  attempt  to  render  harm  

calculable,  play  an  important  role  in  the  classification  and  interrogation  of  disasters,  as  well  

as  their  prevention.39  Acknowledging  the  multiple  valences  of  risk—as  negative  in  its  

identification  of  dangers,  as  positive  in  its  explanation  of  dangerous  activities—situates  

forecasters  as  risk  managers  amid  debates  over  trust,  authority,  and  confidence.40  This  

expertise  is  both  essential  to  their  legitimation  as  scientific  practitioners  but  also  becomes  

a  potential  site  of  controversy  in  their  current  relationship  with  “the  public.”  Outdated  

assumptions  of  what  constitutes  communication  and  expectations  of  public  awareness  and  

preparedness  potentially  leave  the  NWS  agency  as  one  that  values  accuracy  as  a  technique  

of  governmentality41  and  discipline  over  accuracy  as  an  expression  of  care  and  concern.42    

  Finally,  my  approach  is  motivated  by  feminist  epistemologies  that  seek  to  

understand  the  partial,  multiple,  and  situated  nature  of  knowledge,  particularly  the  work  of  

39  Daipha,  “Weathering  Risk:  Uncertainty,  Weather  Forecasting,  and  Expertise”;  Dean,  “Risk,  Calculable  and  Incalculable”;  Douglas  and  Wildavsky,  Risk  and  Culture;  Otway  and  Wynne,  “Risk  Communication:  Paradigm  and  Paradox.”  40  Szerszyniski,  “Risk  and  Trust:  The  Performative  Dimension”;  Lyng,  “Edgework:  A  Social  Psychological  Analysis  of  Voluntary  Risk  Taking.”  41  Foucault,  “Governmentality.”  42  O’Malley,  “Risk  and  Responsibility”;  Foucault,  “Governmentality.”  

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Sandra  Harding  and  her  strong  objectivity.43  In  the  context  of  studying  up,  forecasters  seem  

to  be  located  outside  the  possibility  of  “a  view  from  below.”44  They  are  not  marginalized,  

exempted  from  power,  oppressed  in  the  larger  cultural  norms  that  classify  such  

characteristics  by  their  opposition  to  dominant  structures.  Yet  within  the  confines  of  a  

bureaucratic  institution,  one  that  reduces  their  lives  to  numerical  calculations  of  success  or  

failure,  to  institutional  policies  of  risk  assessment  and  communication  that  make  individual  

humans  invisible  to  the  larger  world,  forecasters  have  much  to  offer.  As  bodies  subject  to  

the  vagaries  of  shift  work  that  can  dehumanize  their  efforts,  forecasters  share  with  the  

populations  they  serve  certain  vulnerabilities:  I  have  seen  women  issue  warnings  during  

Braxton  Hicks  contractions,  for  example;  I  have  heard  stories  of  men  having  seizures  

because  of  the  difficult  hours.  As  bodies  subject  to  trauma,  I  have  listened  to  forecasters  

talk  about  the  frustrations  of  failure,  anxieties  about  their  futures,  and  guilt  over  deaths  

they’ve  witnessed.  Adding  their  stories  to  others  common  in  public  accountings  of  disaster  

fleshes  out  more  fully  the  images  of  objectivity  strongly  cast  by  justice.  

   

Manuscript  Overview  and  Justification    

  As  a  scholar  who  plans  to  seek  employment  in  disciplines  (e.g.  geography)  and  

institutes  (e.g.  NCAR)  that  value  article  publications  over  single-­‐authored  monographs,  I  

have  selected  to  complete  the  manuscript  format  of  the  dissertation.  This  dissertation  

follows  the  requirements  of  the  manuscript  format  of  the  Virginia  Tech  Graduate  School  

and  the  Department  of  Science,  Technology,  and  Society.  Accordingly,  it  includes  a  

43  Harding,  “Rethinking  Standpoint  Epistemology:  What  Is  ‘Strong  Objectivity’?”;  Harding,  Objectivity  and  Diversity:  Another  Logic  of  Science.  44  Harding,  “Standpoint  Theories:  Productively  Controversial.”  

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minimum  of  three  (I  include  four)  “high  caliber,  professional  quality  papers  with  

surrounding  text  providing  context,  theoretical  grounding,  and  coherence”  and  it  offers  a  

“well  formed  body  of  work”  oriented  toward  “peer  reviewed  journals.”45’  The  connective  

tissue  that  adheres  each  article  in  this  dissertation  can  be  found  in  brief  commentaries,  or  

prologues,  written  before  each  article,  which  offer  the  reader  a  meta  view  of  the  article’s  

purpose,  audiences,  targeted  publication  venue,  and  theoretical  and  thematic  linkages  to  

adjacent  articles.      

  Conceptually,  these  articles  are  interconnected  through  an  analysis  of  key  ethical  

dimensions  that  arise  within  the  weather  warning  discourse,  including  my  own  critical  

participation  in  both  the  meteorological  and  disaster  STS  communities.  National  Weather  

Service  forecasters,  agency  documents,  and  warnings  literature.  They  examine  these  ethics  

through  different  scales—the  historical,  the  personal,  the  bureaucratic,  and  the  reflexive—

and  each  is  written  for  a  different  audience  and  to  different  ends.  My  normative  

commitment  is  to  the  forecasters,  their  ways  of  knowing  and  their  view  from  below.  In  

part,  I  do  so  by  following  those  in  Science  and  Technology  Studies  whose  work  identifies  

“dominant  images”  of  forecasters  and  their  profession  and  “makes  visible”46  alternative  

possible  futures.  I  have  also  identified  important  norms  and  values  embedded  and  

emergent  in  their  work  and  hope  to  intervene  in  fruitful  ways  that  demonstrate  what  these  

commitments  bring  on  board  with  their  work.    Thus,  my  work  cultivates  a  conversation  

with  this  community  and,  I  hope,  pushes  STS  to  reach  out  to  less  known  communities  of  

45  STS  Policy  Committee,  “Department  of  Science  and  Technology  in  Society  Graduate  Program  Rules  and  Procedures.”  46  Downey,  The  Machine  in  Me:  An  Anthropologist  Sits  Among  Computer  Engineers,  5,  18–30;  Downey,  “What  Is  Engineering  Studies  For?  Dominant  Practices  and  Scalable  Scholarship,”  2009.  

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practice  and  engage  unconventional  genres  and  styles  as  a  mode  of  intervention.  This  

“making  and  doing”47  necessitates  that  I  occasionally  submerge  STS  jargon,  concepts,  and  

framings  to  meet  forecasters  and  other  non-­‐STS  readers  where  they  are  and  to  travel  with  

them  through  their  concerns,  hopes,  and  fears.    

  Empathetic  Accuracy  

  I  chose  for  the  title  of  my  dissertation  a  phrase  that  often  appears  in  forecaster  

discussions  about  their  predictions:  to  err  on  the  side  of  caution.48  Throughout  my  

fieldwork,  this  same  phrase  has  been  used  as  a  common  reminder  not  to  simply  follow  the  

meteorological  guidelines  for  issuing  warnings  and  communicating  risks.  Instead,  

forecasters  are  free  to  sacrifice  agency  metrics  of  success  to  ensure  that  people  in  harm’s  

way  might  be  safe.  In  effect,  they  are  putting  safety  above  accuracy.  For  example,  during  my  

first  month  at  a  National  Weather  Service  office,  I  attended  a  presentation  that  one  of  the  

management  team  had  put  together  for  the  rest  of  the  staff.  Together,  they  examined  the  

warnings  they’d  issued  that  spring  in  order  to  examine  how  well  these  polygons  mapped  to  

the  local  storm  reports  that  verify  their  warnings.  A  few  phenomena  couldn’t  be  confirmed,  

which  spurred  a  lengthy  exchange  about  the  merits  of  issuing  warnings  for  areas  like  

national  forests,  where  people  are  unlikely  to  live  or  be  during  a  storm.    

  “Shouldn’t  we  not  issue  a  warning  for  an  unpopulated  area  so  the  storm  can’t  be  

verified  anyway?”  one  forecaster  asked.  

47  Downey  and  Zuiderent-­‐Jerak,  “Making  and  Doing:  Engagement  and  Reflexive  Learning  in  STS.”  48  National  Oceanic  and  Atmospheric  Administration,  “National  Weather  Service  Policy  Directive  1-­‐10:  Managing  the  Provision  of  Environmental  Information”;  Englund,  “Forecaster:  We  Erred  on  the  Side  of  Caution”;  Breslin,  “10  Things  the  National  Weather  Service  Wants  You  to  Know  about  Winter  Weather  Forecasts.”  

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  The  Meteorologist  in  Charge,  a  tall,  lanky  man,  stood  up.  “We  need  to  find  a  sweet  

spot,”  he  said,  between  issuing  tornado  warnings  for  unpopulated  areas  that  might  require  

forecasters  to  spend  “hundreds  of  hours”  tracking  down  evidence  to  justify  the  warning.  

“But  we  also  have  to  err  on  the  side  of  caution,”  he  said,  just  in  case  someone  is  there  under  

that  storm,  unaware  of  the  danger.  A  kind  of  hypothetical  thinking,  it  asks  forecasters  to  

imagine  their  publics  and  consider  the  consequences  of  following  rules  or  procedures  or  

even  the  most  likely  scenario.  It  asks  them  to  imagine  that  one  person  who  might  be  out  

there,  who  might  not  see  the  storm  or  know  how  bad  it  could  be.  

  I  evoke  the  phrase  “err  on  the  side  of  caution”  to  demonstrate  the  tension  between  

accuracy  and  care.    “Caution,”  as  I  understand  it,  is  not  about  precision.  Instead,  it  is  about  

concern  for  people  potentially  in  harm’s  way.  It  values  people  over  numbers.    And  it  

highlights  the  sense  of  responsibility  forecasters  feel  for  people’s  safety49  in  light  of  this  

concern,  an  obligation  to  the  people  in  their  communities  often  at  the  expense  of  their  

metrics  of  accuracy.  The  Meteorologist  in  Charge,  then,  balanced  for  the  group  their  

collective  commitment  to  accuracy  and  their  commitment  to  people—to  caring  for  both  at  

once.  It  is  a  care  through  accuracy  that  takes  into  account  concern  for  people.    “To  err  on  

the  side  of  caution”  likewise  points  to  the  multiplicity  of  ethics  that  emerge  alongside  and  

in  response  to  one  another.    

   

  Selecting  this  phrase  as  my  title  also  points  to  my  intent  with  the  four  ethics  

explored  within  the  following  and  how  they  speak  to  my  larger  project  of  revealing  how  

accuracy  and  care  already  exist  in  their  practices,  often  together  and  inseparably.    I  term   49  Morss  et  al.,  “Flash  Flood  Risks  and  Warning  Decisions:  A  Mental  Models  Study  of  Forecasters,  Public  Officials,  and  Media  Broadcasters  in  Boulder,  Colorado,”  2015,  2021.  

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this  interrelated  set  of  ethics  “empathetic  accuracy”  as  a  way  of  illustrating  the  

interconnectedness  of  accuracy,  care,  resilience,  and  relationality  more  explicitly.50  It  calls  

attention  to  the  fact  that  these  values  are  entangled  in  forecasting  science,  co-­‐constituted  in  

forecasters’  technological  developments  and  policies,  and  reflected  in  the  activities  that  

direct  their  interactions  with  different  publics.  In  fact,  an  etymology  of  accuracy  in  early  

sixteenth  century  Latin  is  accuratia,  or  to  care  or  give  attention,  as  in  “executed  with  great  

care.”51  Perhaps  ironically,  then,  the  linguistic  root  of  the  term  accuracy  contains  care.  But  

accuracy  as  a  term  in  forecasting  is  so  overly  burdened  by  scientized  connotations  about  

precision,  truth,  and  objectivity,  that  it  is  difficult  to  merely  point  to  its  roots  and  have  that  

suffice  as  a  way  of  enrolling  forecasters  to  consider  care  as  an  important  ethic  in  their  

labor.    

  Article  Overview  and  Interconnectedness  

  The  selection  of  the  ethics  highlighted  in  this  dissertation  does  some  of  this  work.    I  

develop  my  case  through  a  series  of  articles  written  at  different  scales  to  emphasize  the  

dimensionality  of  ethics  in  my  own  examination  of  the  warning  process;  it  echoes  the  

various  ways  I  have  observed  them  in  the  operational  setting.  I  begin  with  a  genealogical  

account  of  “the  man-­‐machine  mix”  as  an  articulation  of  accuracy  that  continues  to  

perpetuate  an  identity  of  forecasters  that,  I  suggest,  has  never  been  appropriate  to  them.  

Because  variations  of  this  term  (e.g.  man-­‐machine,  human-­‐machine,  human  element)  are  

still  used  today,  it  continues  to  frame  what  forecaster  can  and  ought  to  be.  It  also  masks  

50  I  wish  to  distinguish  this  term  from  empathic  accuracy,  a  term  coined  in  psychology  by  William  Ickes  and  William  Tooke  in  1988.  It  refers  to  how  accurately  one  person  can  infer  the  thoughts  and  feelings  of  another  person  based  on  concepts  like  “affect  sharing”  and  “mentalizing.”  See  Ickes  &  Tooke  (1988)  and  Ickes  (2003).  51  “Accuracy.”  

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what  the  second  article  highlights,  which  is  the  ethic  of  care,  a  value  that  is  revealed  most  

clearly  in  moments  of  crisis  but  exists  in  their  daily  work,  as  well.  Forecasters  are  bound  by  

care  in  their  desire  to  protect,  as  this  article  shows  from  a  more  intimate  point  of  view:  that  

of  specific  forecasters  and  their  publics  during  a  tornado.  By  scaling  down  to  see  care  in  

action,  this  article  offers  a  glimpse  of  an  alternative  ethic  that  might  be  more  visible  in  their  

professional  identities,  too.    The  third  article  takes  a  future-­‐oriented  perspective  at  the  

institutional  level  and  is  more  normative  in  its  orientation  about  what  I  believe  forecasters  

ought  to  be.  The  National  Weather  Service’s  newest  initiative  Impact  Based  Decision  

Support  Services  (IDSS)  offers  an  opportunity  to  emphasize  empathetic  accuracy.  Part  of  

the  fundamental  changes  the  agency  envisions  among  their  forecasting  staff  is  a  philosophy  

emphasizing  the  need  to  develop  “deep  relationships”  with  their  partners.  Through  

multiple  valences  of  resilience—e.g.,  knowing  people  well  enough  to  understand  what  they  

are  most  vulnerable  to  and  thus  would  recover  from—IDSS  has  the  potential  to  make  more  

apparent  how  resilience  operates,  and  could  do  more,  within  their  practices.  It  likewise  

reveals  a  mechanism  for  deploying  empathetic  accuracy.  

  I  wrote  the  final  article  with  a  colleague  in  the  Disaster  STS  community  to  

acknowledge  that  work  in  disaster  contexts  (and  disaster  is  a  contested  term,  of  course)52  

is  one  of  compromise.    This  is  most  clearly  demonstrated,  for  me,  through  an  ethic  of  

relationality,  or  a  commitment  to  ongoing  responsibility  for  researcher  actions  and  how  

they  affect  others  that  we  work  with.  It  challenges  us  every  day  to  balance  ethics  valued  in  

university  settings,  like  Institutional  Review  Boards  or  imperatives  to  publish  in  scholarly   52  Fortun,  Advocacy  after  Bhopal:  Environmentalism,  Disaster,  New  Global  Orders;  Knowles,  The  Disaster  Experts;  Petersen,  “Producing  Space,  Tracing  Authority:  Mapping  the  2007  San  Diego  Wildfire”;  Liboiron,  “Disaster  Data,  Data  Activism:  Grassroots  Responses  to  Representations  of  Superstorm  Sandy,”  2015.  

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journals,  with  an  obligation  for  mutual  respect  and  dignity  between  myself  and  the  

forecasters  I  participate  with.  It  compels  me  every  day  to  scrutinize  what  I  write  and  how  I  

critique—with  “great  care  and  caution”53—those  with  whom  I  participate.  Relationality,  

then,  offers  yet  another  aspect  of  the  ethic  of  care  operating  within  the  larger  weather  

community,  where  most  are  dedicated  to  protection  of  life.  This  is  not  unlike  the  position  of  

compromise  forecasters  face  in  their  own  work.  Forecasters  stand  in  between  their  

commitments  to  accuracy  and  care,  between  science  and  safety,  and  between  their  history  

and  their  future.    

  Forecasters  and  I  are  in  this  endeavor  together,  sharing  the  same  stakes,  a  sense  of  

enthusiasm,  and  passion  for  figuring  out  how  best  to  serve  one  another  and  those  in  our  

publics.  They  deserve  a  complex  and  reflexive  accounting  of  successes  and  failure  and  they  

expect  those  of  us  who  are  analysts  of  their  work  to  hold  ourselves—and  one  another—

accountable.  To  my  colleagues  and  friends  in  the  weather  community  who  have  asked  on  

many  occasions  for  my  help  in  these  efforts,  I  offer  the  following  articles  as  a  place  to  begin  

that  conversation.  

 

53  Latour,  “Why  Has  Critique  Run  Out  of  Steam?  From  Matters  of  Fact  to  Matters  of  Concern,”  246.  

1

Article  1:  The  Ethic  of  Accuracy:  Troubles  in  The  Man-­‐Machine  Mix    

 Prologue       I  first  heard  the  term  “meteorological  cancer”  during  the  second  week  of  

observations  of  a  local  forecast  office  four  years  ago.  We  were  talking  about  a  new  initiative  

to  consolidate  offices  across  the  National  Weather  Service,  condensing  them  from  the  

current  122  local  offices  to  a  few  regional  hubs.  It’s  actually  an  old  idea,  one  that  gets  

revisited  during  threats  of  funding  and  questions  about  why  forecasters  in  public  

government  service  matter.  This  forecaster,  however,  talked  about  how  one  of  the  reasons  

for  such  a  possible  consolidation  was  the  overreliance  of  younger  forecasters  on  computer  

models  in  their  daily  work.  Instead  of  developing  their  own  conceptual  model,  or  snapshot  

of  the  weather,  they  were  simply  taking  computer  model  predictions  of  different  weather  

variables  and  using  them  in  their  forecast.  In  effect,  he  said,  “Forecasters  are  substituting  

the  computer  model  for  their  own  knowledge,”  a  problem  he  called  “meteorological  

cancer.”  As  a  metaphor  of  terminal  illness,  the  phrase  struck  me  as  an  important  vehicle  

into  current  feelings  about  the  future  of  forecasting  as  a  profession.  The  tenor  of  the  

metaphor  suggests  that  forecasters  are  contributing  to  their  own  demise,  as  the  computer  

models,  in  effect,  “eat  away”  at  their  expertise  and  relevance.  The  vehicle  of  the  metaphor,  

cancer,  implies  this  problem  that  will  spread  like  a  disease  and  eventually  kill  the  

profession.  The  phrase  “automated  out  of  a  job,”  in  fact,  often  accompanies  the  metaphor  as  

a  kind  of  prognosis.    

2

  I’ve  been  privy  to  other  ideas  that  are  degrading  to  forecasters  are  and  do.  There  are  

those  terms  that  highlight  the  role  of  forecasters  “over  the  loop,”  for  example,  making  them  

managers  in  the  forecasting  process.  Here  they  exert  their  expertise  to  “quality  control”  

data  as  it  proceeds  throughout  a  mechanized  system.  In  other  contexts,  they  are  mere  

communicators,  “hand  holders”  for  the  public  as  they  explain  forecasts  more  clearly  so  that  

decisions  can  be  made.  They  are  caught,  in  a  way,  between  the  machines  that  facilitate  their  

job  and  the  people  who  rely  on  them,  cast  neither  as  fully  scientist  nor  public  servant.    

  Forecasters  are  cyborgs  in  this  “leaky”  distinction  between  human  and  machine,  

unclear  “who  makes  and  who  is  made”  in  this  social  reality.54  Currently,  forecasters  in  the  

NWS  are  somewhere  in  the  loop  of  man-­‐machine,  though  recent  initiatives  like  Impact  

Based  Decision  Support  Services—addressed  in  article  3  in  this  dissertation—are  moving  

the  humans  more  fully  outside  the  loop  to  manage  it.  More  disturbingly  for  some  

forecasters  is  the  notion  that  their  main  job  will  then  be  to  quality  control  the  models  and  

communicate  their  meaning  to  different  decision  makers—a  job  they  engage  with  to  some  

degree.  To  do  this  new  job  well,  however,  necessitates  that  forecasters  learn  more  about  

decision  makers,  their  thresholds,  and  their  “needs”  and  then  tailor  expert  explanations  to  

these  people.  In  effect,  it  potentially  transforms  the  meteorologist  as  scientist  into  the  

communicator—a  demotion  in  skill  for  those  whose  passion  is  prediction.    

  The  term  “man-­‐machine  mix”  entered  the  conversation  much  later  for  me  as  I  was  

completing  my  fieldwork  in  2016—a  moment  I  recount  in  the  following  article.  As  I  looked  

for  the  origins  of  this  phrase,  I  discovered  it  co-­‐occurred  with  “meteorological  cancer”  in  

the  same  set  of  writings  by  Leonard  Snellman,  an  influential  forecaster  working  in  the   54  Haraway,  “A  Manifesto  for  Cyborgs:  Science,  Technology,  and  Socialist  Feminism  in  the  1980s,”  193,  221.  

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National  Weather  Service  in  the  latter  half  of  the  20th  Century.  He  wrote  about  the  “man-­‐

machine  mix”  as  early  as  1969  in  his  attempt  to  explain  the  hybridity  of  humans  and  

computers  that  had  become  part  of  forecaster’s  work.  By  machine,  he  specifically  meant  

two  things:  first,  the  computer  model,  a  new  simulation  technology  that  embodied  accuracy  

and  speed,  two  key  values  of  predictive  science;  and  second  the  forecasters’  minicomputer,  

or  workstation,  which  he  felt  balanced  the  equation  of  the  mix,  allowing  forecasters  to  

complement  computer  models  with  their  expertise.  If  abused,  computer  models  could  

become  a  malignancy  in  their  practices.  Invoking  disease,  cybernetics,  irrelevancy,  and  

promise,  the  “man-­‐machine  mix”  and  “meteorological  cancer”  offer  rich  concepts  that  help  

me  explore  what  forecasters  value—a  discourse  that  still  resonates  in  the  community  

today.  

  But  how  to  explore  these  concepts  in  ways  that  connect  them  to  knowledge  

production  in  the  profession  of  forecasting?  One  way  is  to  examine  those  scientific  values  

they  invoke  and  that  have  successfully  resonated  with  forecasters  for  over  50  years.  One  

such  candidate  revealed  in  the  intersection  of  human  and  machine  is  accuracy,  a  core  value  

in  sciences  generally  and  a  seminal  one  in  prediction  specifically.  I  argue  that  the  “man-­‐

machine  mix”  is  an  articulation  of  accuracy  in  the  practices  and  discourses  of  the  

forecasting  profession  that  points  to  an  image  of  forecasting  science.  By  demonstrating  

accuracy  as  a  social  product  and  overly  emphasized  dimension  of  the  “man-­‐machine  mix,”  I  

hope  to  likewise  reveal  that  accuracy  is  a  dominant  ethic  in  forecasting  that  continues  to  be  

re-­‐inscribed  in  the  discourse  of  meteorology.  On  one  level,  it  does  so  through  the  frame  of  

competition,  of  forecasters  rivaling  computer  models  for  daily  work  even  as  the  machines  

increasingly  outperform  them.    The  timeliness  and  accuracy  of  warnings  saves  lives,  which  

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is  one  reason  to  invest  in  them,  but  it  also  saves  money,  in  terms  of  losses  that  might  be—

agricultural,  infrastructural,  personal,  and  the  like.  That  is,  “man-­‐machine  mix”  is  a  term  of  

political  economy.  On  another  level,  it  does  so  at  the  expense  of  other  ethics  that  when  

deployed  alongside  accuracy  may  better  match  the  profession’s  main  goal  of  protecting  

lives.  In  article  two,  I  suggest  the  value  they  should  emphasize  is  care.  

  For  a  publication  venue,  I  chose  the  journal  Technology  and  History,  a  peer-­‐reviewed  

journal  published  quarterly  by  Taylor  and  Francis.  According  to  the  journal’s  submission  

guidelines,  the  journal  is  a  “forum  for  research  on  technology  in  history,”  and  they  welcome  

historical  contributions  that  explore  a  “wide  frame”  of  technology  “as  knowledge,  practice,  

and  material  resource”  through  analytic  and  critical  approaches.  They  encourage  a  broad  

range  of  disciplinary  contributions  that  offer  dialogue  between  history  of  technology  and  

the  humanities.    In  their  submission  category,  Historiographic,  Field,  and  Thematic  Essays  

they  refine  their  goal  as  one  that  “offers  critical  reflection  on  a  broad  sweep  of  intellectual  

activity  and  engages  common  concerns  in  explanation  within  history  of  technology  and  

other  scholarly  fields.”    Submissions  are  limited  to  40  pages,  including  references  and  

figures.      

  Tracing  the  genealogy  of  the  concepts  “man-­‐machine  mix”  and  “meteorological  

cancer”  in  the  context  of  automation  is  an  historical  and  cultural  endeavor.  Although  I  open  

the  article  with  a  contemporary  scene  from  my  fieldwork  in  a  forecast  office,  I  quickly  

address  the  concept  in  its  historical  context,  illustrating  its  attending  concerns  as  part  of  a  

larger  societal  anxiety  about  automation  in  the  1960s.  Seen  in  this  light,  the  “man-­‐machine  

mix”  is  one  specific  instance  of  a  larger  sociotechnical  frame  for  addressing  the  prevalence  

of  computers  in  the  United  States  and  the  apocalyptic  fears  of  artificial  intelligence  and  

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computer  domination.  While  forecasters  are  not  undereducated  and  unskilled  workers  

common  on  assembly  lines  in  factories  or  mechanized  manufacturing  of  the  early  20th  

century—the  common  setting  for  what  was  termed  “automation  anxiety”—in  the  1950s  

many  entered  the  profession  of  meteorology  from  the  military  without  formal  education  in  

sciences  and  as  skilled  technicians  facing  increasingly  more  complex  routines  involving  

computer  models.    

  For  scholars  in  Science  and  Technology  Studies,  I  offer  this  discussion  as  a  case  

study  of  how  anxiety  over  the  loss  of  scientific  authority  manifested  itself  in  this  specific  

profession  during  the  latter  half  of  the  20th  Century.  For  current  and  future  weather  

forecasters,  I  offer  this  article  to  contextualize  their  concerns  about  the  future  of  their  

profession  and  to  suggest  they  needn’t  allow  these  concepts  and  values  to  uncritically  affect  

their  role  in  relationship  to  society.  They  can  (and  should)  consider  other  images  of  their  

profession  that  better  reflect  their  responsibility  to  their  publics  and  their  beliefs  about  

their  function  as  public  servants  in  their  communities.    

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The  Ethic  of  Accuracy:  Troubles  in  the  Man-­‐Machine  Mix    

By  Jen  Henderson  

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 Abstract  (150  Words)  

   

  By  the  1960s,  computer  models  had  begun  to  improve  the  predictive  accuracy,  and  

thus  scientific  legitimacy,  of  National  Weather  Service  (NWS)  forecasters.  One  operational  

meteorologist,  Leonard  Snellman,  conceptualized  this  optimistic  collaboration  between  

humans  and  nonhumans  as  the  “man-­‐machine  mix.”  I  argue  that  this  image  came  to  

represent  forecasting  scientists  and  enabled  them  to  mount  an  ethic  of  accuracy,  which  

continues  as  a  dominant  value  in  their  work.  This  scientific  persona,  however,  relies  on  a  

clear  demarcation  between  humans  and  machines,  one  that  is  continually  troubled  by  the  

growing  power  of  computer  models.  In  the  tradition  of  Foucault,  I  trace  a  genealogy  of  the  

“man-­‐machine  mix”  to  demonstrate  that  anxieties  emergent  in  its  evolution,  such  as  fear  

over  automation,  reflect  limitations  of  forming  an  identity  around  a  single  overriding  ethic.  

This  image  leaves  out  other  ethics  relevant  to  their  practice  that,  as  computer  model  

precision  increases,  relegate  them  to  a  future  profession  centered  on  communication,  

narrowly  defined  as  information  transfer.      

 

Keywords:  Accuracy,  weather  forecasting,  values,  automation    

 

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The  Ethic  of  Accuracy:  Troubles  in  the  Man-­‐Machine  Mix    

 Weather  forecasting  is  evolving  in  a  world  characterized  by  accelerating  scientific  and  technological  change…  [which]  has  led  to  some  confusion  and  concern  about  the  role  of  humans  in  forecasting  the  weather.     -­‐-­‐Charles  Doswell,  “The  Human  Element  in  Weather  Forecasting,”  1986  

 

  In  early  spring  of  2016,  I  sat  with  meteorologists  in  a  National  Weather  Service  

(NWS)  forecast  office  in  the  southeastern  United  States.  They  had  been  issuing  flash  flood  

and  tornado  warnings  for  several  hours  within  the  boundaries  of  their  county  warning  

area,  the  geopolitical  space  over  which  they  have  responsibility.  The  last  storm,  

represented  as  a  splotchy  mass  of  red  and  green  pixels  on  a  computer  screen,  had  just  

crossed  the  invisible  boundary  line  into  an  adjoining  office’s  jurisdiction  when  we  began  to  

talk  about  new  software  technologies  being  developed  at  the  National  Severe  Storms  

Laboratory,  a  weather  prediction  test  bed  in  Norman,  Oklahoma.  They  explained  to  me  that  

the  lab  is  creating  algorithms  to  help  automate  weather  warnings.  Eventually.  “Eight  to  ten  

years  from  now,”  one  of  them  emphasized.  

  “You  mean  you  won’t  be  responsible  for  warnings?”  I  asked,  surprised.  “They’ll  be  

automated?”    

  A  lead  forecaster,  Mark,55  laughed  a  bit.  “Don’t  say  that  too  loud,  Jen.  That  at  some  

point  we  won't  be  doing  the  warnings.”  I  had  been  conducting  ethnographic  observations  of  

forecasters  in  three  other  offices  over  the  past  year  and  knew  well  the  importance  of  the  

warning  as  an  object  of  epistemological  authority  in  NWS  forecaster  work.  Warnings  are  

spatiotemporal  alerts  about  dangerous  weather  that  forecasters  create  at  their  

55  Names  and  identifying  information  has  been  changed  to  protect  participants  in  my  research.  

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workstations  and  distribute  through  their  proprietary  software  systems.  Their  success  

relies  on  notions  of  accuracy  and  timeliness  on  many  fronts:  accuracy  of  threat  type,  the  

threat’s  location  and  magnitude,  as  well  as  speed  in  detecting  and  constructing  alerts  

before  such  threats  affect  people.  In  sum,  forecasters’  warnings  generate  knowledge  about  

risks  to  life  that  should,  ideally,  enable  others  to  act,  whether  action  comes  from  emergency  

managers  and  other  public  safety  officials,  or  members  of  their  lay  publics.  Warnings,  then,  

help  establish  the  societal  relevance  of  NWS  forecasters  as  scientists  who  successfully  fulfill  

their  agency’s  mission  to  “protect  life  and  property.”56    

  References  made  in  our  exchange  to  the  automation  of  daily  weather  forecasting  

had  become  a  familiar  concern  to  me,  one  I’ve  encountered  a  number  of  times  when,  during  

shift  work,  operational  meteorologists  talk  about  the  possibility  of  computer  models  

replacing  the  work  of  forecasters.  It  is  a  concern  with  a  history,  one  that  intensified  in  the  

United  States  during  the  1960s  as  people  contemplated  the  role  of  machines  in  their  lives.  

With  roots  in  the  labor  disputes  that  erupted  across  the  country  during  World  War  II,  

automation  practices  developed  through  technological  advances  and  shop-­‐floor  politics  

that  pitted  unions  against  management  and  put  the  control  of  the  machine  and  the  skill  of  

the  worker  in  center  stage.57  As  historian  David  Noble  notes  of  this  entanglement,  a  

“shortage  of  skilled  workers,  engendered  in  part  by  automation  itself,  had  now  become  the  

supreme  justification  for  more  automation.”58  Automation  produced  a  number  of  

ontological  fears  and  professional  anxieties.  One  writer  in  the  1960s,  for  example,  proposed  

that  what  people  object  to  in  automation  is  not  so  much  the  loss  of  labor  but  the  change  in   56  “Weather  Ready  Nation:  NOAA’s  National  Weather  Service  Strategic  Plan.”  57  For  a  thorough  treatment  of  automation  in  the  U.S.,  see  Noble,  Forces  of  Production:  A  Social  History  of  Industrial  Automation.  58  Ibid.,  41.  

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the  image  of  themselves.  “There  is  a  strong  revulsion  in  many  people  against  admitting  the  

possibility  of  machines  behaving  like  human  beings;  they  feel  that  this  would  be  equal  to  

admitting  that  ‘man  is  a  machine.’”59  For  forecasters,  such  an  idea  cuts  to  the  very  heart  of  

their  efforts  to  demonstrate  their  value  to  society  as  scientific  experts.    

  In  the  local  forecast  office  that  day,  news  that  the  National  Weather  Service  had  

been  developing  mechanisms  to  automate  warnings  surprised  me.  “Warnings,”  as  one  had  

said  to  me,  “are  the  last  bastion  of  the  forecaster”  against  the  relentless  precision  of  the  

computer.    I  found  myself  repeating  the  question  that  most  often  accompanied  such  fears:  

“So  what  would  you  all  be  doing?”  I  asked.  What  would  the  role  of  a  weather  forecaster  be  if  

they  are  largely  replaced  in  daily  prediction  by  computer  models  and  increasingly  

outperformed  in  their  accuracy  and  timeliness  of  warnings?  In  the  parlance  of  the  

forecasters,  what  “value  added”  might  they  contribute?    

  Mark  looked  at  me  and  shrugged,  “Essentially  managing  [the  machines].  And  

messaging.”  Amid  the  talk  of  competition  between  humans  and  their  technologies,  then,  

emerges  a  tension  between  the  success  of  their  work  as  predictive  experts,  which  computer  

models  help  facilitate,  and  the  value  of  their  own  expert  skill  in  the  process.  At  stake  are  the  

identities  of  forecasters  as  scientists  and  the  survival  of  their  profession  in  ways  they  

envision  it  ought  to  exist.    

  But  there  was  one  more  element  of  this  picture  that  I  had  been  missing  for  me  in  

understanding  their  anxiety  over  professional  loss.  Scott,  the  forecaster  sitting  closest  to  

me,  chimed  in:  “The  man-­‐machine  mix  is  essentially  what  it's  going  to  be.”  The  others  

nodded  in  agreement.    

59  Gabor,  “Inventing  the  Future,”  142.  

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  Scott  explained  the  term  “man-­‐machine  mix”  as  a  “kind  of  artificial  intelligence,”  

which  I  would  come  to  understand  as  a  hybrid  assemblage  of  mathematical  equations,  

scientific  knowledge  about  the  atmosphere,  and  technological  infrastructures  of  the  

computer  living  alongside  human  expertise,  training,  and  experience.  The  concept  sounded  

like  something  out  of  1950s  science  fiction,  some  futuristic  vision  of  cyborgs,  part  human,  

part  machine.  In  Scott’s  explanation,  however,  the  image  was  much  more  pedestrian.    

Some  of  the  testing  at  the  [Norman]  test  bed  has  found  that  machines  and  algorithms  [do]  a  lot  better  than  the  human  at  more  quickly  identifying  and  tracking  the  hail  cores.  And  it's  about  crunching  numbers,  it's  that  you  know  the  environment…  For  example,  there's  always  a  trigger  for  us:  What's  going  to  produce  quarter  inch  hail?  The  machine's  already  going  to  know  that  and  it's  going  to  see  it  before  you  see  it.  And  it  will  track  it  and  do  it  better  than  you  can—more  often,  too.    

Using  a  test  bed  to  experiment  with  different  combinations  of  humans  and  machines  in  the  

warning  process  suggests  that  forecasters’  framing  of  the  mix  as  competition  with  

computer  models  is  not  without  merit.  Findings  from  the  test  bed  note  the  ways  in  which  

one  is  better  than  the  other  on  different  registers,  and  conclusions  about  the  different  

elements—human,  machine,  man-­‐machine—influence  decisions  about  where  forecasters  

belong  in  their  work.    

  Likewise,  laboratory  testing  suggests  a  search  for  ways  to  clearly  distinguish  what,  

in  forecasting  practice,  is  intimately  entangled.  In  a  forecasting  office,  boundaries  between  

human  and  computer  are  fluid,  blurred,  and  multiple.  There  is  no  single  human  nor  a  

solitary  machine  but  a  plurality  of  both;  there  is  no  unidirectional  process  of  humans  and  

machines  but  a  folding  of  interactions  whereby  humans,  for  example,  inscribe  algorithms  in  

machines  based  on  mechanized  observational  systems,  which  then  generate  several  

computer  models  to  produce  an  array  of  possible  futures.  Humans  select  from  these  

outcomes,  integrating  their  possibilities  with  new  observations  taken  from  a  variety  of  

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machines,  as  well  as  their  own  experiences  and  judgments.  Machines  become  a  way  to  

visualize  the  atmosphere,  meaning  forecasters  see  through  the  eyes  of  those  who  created  

the  algorithms,  their  workstations,  and  their  software.  Together,  these  sociotechnical  

arrangements  create  something  that  might  be  better  characterized  as  a  hybridity  of  

“humachine”  assemblages.  In  the  concept  of  the  man-­‐machine  mix,  then,  the  notion  of  a  

“mix”  is  more  plausible  than  a  hyphenated  adjective  that  suggests  partitioning  the  two.  So  

why  work  so  hard  to  keep  man  and  machine  separate?  

  Continual  efforts  made  to  demarcate  humans  from  machines  are  important  to  

understanding  the  image  of  the  forecaster  as  scientist,  which  is  how  forecasters  envision  

themselves  as  experts.  As  scientists,  forecasters  must  meet  the  standards  of  good  science  

whereby  their  predictions  can  be  verified  and  thus  meet  claims  of  accuracy.  Yet,  accuracy  

itself  is  difficult  to  define  since  its  meaning  depends  on  one’s  point  of  view  and  techniques  

of  definition  used.  What  is  accurate  to  the  forecaster  in  terms  of  correctly  labeling  a  threat-­‐-­‐

a  tornado  versus  strong  winds,  for  example-­‐-­‐might  be  meaningless  to  people  who  

experienced  damage  to  their  home.  Accuracy  for  people  in  the  community  might  mean  how  

quickly  they  got  a  warning  that  told  them  about  the  kind  of  danger  they  might  expect.  For  

the  purposes  of  this  article,  then,  I  define  an  ethic  of  accuracy  as  the  value  forecasters  place  

on  correctly  identifying  unfolding  meteorological  conditions  before  weather  phenomena  

occurs  and  potentially  effects  people  in  their  communities.  It  is  a  definition  that  reflects  

common  notions  of  accuracy  found  in  the  ways  forecasters  count  accuracy  in  their  metrics  

of  success  and  in  their  narratives  of  competition  with  computer  models.60  

60  Metrics  of  success  dictated  by  the  Government  Performance  and  Reporting  Act  include  false  alarm  rates,  critical  success  index,  and  probabilities  of  detection.  

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  Another  important  reason  to  demarcate  humans  and  machines,  then,  is  forecasters’  

concerns  over  automation,  or  a  continual  threat  about  being  replaced  by  computer  models  

that  keeps  forecasters  in  competition  with  the  machines.  “Automation,”  Peter  Drucker,  

management  scholar,  wrote  in  1962,  “can  be  defined  simply  though  superficially  as  the  use  

of  machines  to  run  machines.”  But  it  is  more  than  this,  of  course.  Threats  of  automation  also  

limit  the  professional  role  of  the  forecaster.  In  one  version  of  the  future,  forecasters  could  

see  themselves  as  machines,  whether  as  an  extension  of  the  machine  managing  them  from  

“over  the  loop,”  or  distilled  as  a  representative  knowledge  base,  separated  from  their  

bodies  and  mechanically  integrated  with  the  machines.  It  is  an  image  they  have  struggled  

against  for  nearly  forty  years  in  their  publications  and  practices.  As  more  than  one  

forecaster  has  revealed  to  me,  even  today,  many  refuse  to  let  the  computer  models  make  

their  forecasts;  they  prefer  instead  to  work  out  the  forecast  for  every  hour  and  every  day  

for  which  they  are  responsible  on  shift.  “They’re  still  trying  to  make  it  their  forecast,”  one  

member  of  NWS  management  staff  noted.  The  problem  to  solve  here  is  what  a  forecaster  

ought  to  be  and  how  to  find  possible  ways  forward  that  reflect  the  best  of  who  they  already  

are.  Yet,  as  my  article  will  demonstrate,  threats  of  machines  outperforming  humans  

continue  to  overdetermine  possible  forecaster  identities,  ones  that  may  not  entirely  reflect  

forecaster  practices  or  who  they  might  become.      

  Meteorology,  like  other  sciences,  is  a  sociopolitical  enterprise,  and  its  imagery,  

metaphors,  processes,  and  languages  hold  within  them  insight  into  what  scientists  value.  

Scholars  in  Science  and  Technology  Studies  (STS)  have  illustrated  that  instead  of  a  value-­‐

free  and  human  directed  enterprise,  science  is  a  social  process  in  which  many  different  

human  and  non-­‐human  actors  have  agency.  Nor  are  the  practices  of  science  linear  and  

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straightforward  but  complex,  messy,  historically  contingent  and  politically  motivated.  Thus  

it  is  with  the  man-­‐machine  mix  and  the  ethic  of  accuracy.  In  his  book  Inventing  Accuracy:  A  

Historical  Sociology  of  Nuclear  Missile  Guidance,  for  example,  Donald  MacKenzie  reveals  the  

social  aspects  of  missile  accuracy  design  and  how  it  became  “a  product  of  a  complex  

process  of  conflict  and  collaboration  between  a  range  of  social  actors…  that  has  fueled,  and  

has  itself  been  fueled  by,  the  cold  war.”61  Accuracy  in  this  context  imparts  the  motives,  

history,  arguments,  and  everyday  details  of  how  nuclear  guidance  systems  coproduced  

nuclear  society.  The  world  of  forecasting  similarly  offers  a  sociotechnical  window  into  the  

multiplicity  of  thinking  about  accuracy  and  its  instantiations.        

  Phaedra  Daipha  writes  most  pointedly  about  this  ethic’s  circulation  in  operational  

meteorology  contexts  in  her  book  Masters  of  Uncertainty,  noting  that,  “All  [NWS]  

organizational  effort  is  directed  toward  improving  the  accuracy  of  NWS  predictions.”62  She  

explains  how  such  a  focus  puts  at  odds  agency  directives  that  demand  accountability  for  

performance  standards  and  the  pressures  of  public  safety.  She  writes,  “The  essence  of  a  

good  forecast  is  currently  distilled  into  two  metrics:  accuracy  and  timeliness,”  which  bears  

out  most  often  in  the  threat  of  severe  weather.    “Accuracy  concerns,”  however,  “may  be  

silenced  in  the  name  of  public  service.”63  In  other  words,  operational  meteorology’s  daily  

activities  get  divided  between  those  that  justify  forecasters’  scientific  endeavor  and  others  

that  represent  their  dedication  to  serving  society.  Here,  accuracy  functions  as  a  boundary  

object64  that  intensifies  what  the  institution  ought  to  count  as  success  when  lives  are  at  

61  MacKenzie,  Inventing  Accuracy:  A  Historical  Sociology  of  Nuclear  Missile  Guidance,  3.  62  Daipha,  Masters  of  Uncertainty:  Weather  Forecasters  and  the  Quest  for  Ground  Truth,  117.  63  Ibid.,  120.  64  Star  and  Griesemer,  “Institutional  Ecology,  ‘Translations’  and  Boundary  Objects:  Amateurs  and  Professionals  in  Berkeley’s  Museum  of  Vertebrate  Zoology,  1907-­‐39.”  

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stake.  This  tension  between  forecasters’  scientific  praxis  and  responsibility  to  the  public  is  

where  this  article  finds  its  purpose  and  it  is  here  where  the  “man-­‐machine  mix”  and  its  

ethic  of  accuracy  becomes  an  important  concept  to  understand.  

  In  what  follows  I  give  a  genealogical  account  of  the  “man-­‐machine  mix”  and  the  

scientific  persona  of  the  forecaster  it  generates.  I  trace  the  evolution  of  this  image  to  

demonstrate  that  anxieties  evident  today,  in  particular  fears  over  automation,  reflect  the  

limitations  of  forming  an  identity  around  a  single  overriding  ethic.  I  argue  that  the  “man-­‐

machine  mix”  has  kept  the  National  Weather  Service  and  its  forecasters  overly  focused  on  

an  ethic  of  accuracy  and  the  sociotechnical  apparatuses  that  serve  such  interests.  This  

scientized  image  of  forecasting  leaves  out  other  ethics  relevant  to  their  practice  that,  as  

computer  model  precision  has  increased,  relegate  them  to  a  future  profession  primarily  

centered  on  communication,  narrowly  defined  as  information  transfer.    In  this  endeavor  I  

follow  scholars  like  Foucault  who  examined  a  history  of  the  present  through  episodes  in  

the  past.  “A  genealogy,”  he  writes,  “must  be  sensitive  to  [the  term’s]  recurrence,  not  in  

order  to  trace  the  gradual  curve  of  their  evolution,  but  to  isolate  the  different  scenes  where  

they  engaged  in  different  roles.”65  To  this  end,  I  examine  three  “scenes”  where  inflections  of  

the  man-­‐machine  mix  generated  different  roles  for  the  forecaster  as  scientist.    

  I  begin  with  the  origin  of  the  man-­‐machine  mix  in  meteorology  when  forecaster  

Leonard  Snellman  coined  the  term  in  the  late  1960s  to  capture  the  great  optimism  and  

enthusiasm  about  the  growth  of  operational  forecasting  as  a  science.  Next  I  examine  two  

moments  of  significance  for  the  man-­‐machine  mix:  1)  the  mid-­‐1970s  when  the  accuracy  of  

forecast  accuracy  unexpectedly  declined  leading  to  a  potential  loss  of  forecaster  skill  and  

65  Foucault,  Language,  Counter-­‐Memory,  Practice:  Selected  Essays  and  Interviews,  140.  

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profession,  which  forecasters  called  “meteorological  cancer;”  and  2)  the  mid-­‐1990s  when  

meteorologists  grappled  with  how  to  identify  forecaster  value  either  by  differentiating  

from  machines  their  unique  contributions  and  value  to  users  or  integrating  their  judgment  

and  expertise  fully  into  the  machines.  I  show  how  these  moments  have  led  to  a  multiplicity  

of  current  articulations  of  accuracy  as  they  operate  in  forecasting  discourse.  I  conclude  by  

suggesting  that,  in  practice,  forecasters  have  never  been  exemplified  well  by  an  exclusive  

emphasis  on  the  ethic  of  accuracy,  nor  should  they  continue  to  believe  they  have  to  be.    

  Man-­‐Machine  Mix  as  Optimistic  Vision    

  In  the  mid-­‐1950s  a  new  machine  called  the  electronic  computer  transformed  the  

profession  of  operational  forecasting.  Based  on  an  agenda  established  by  the  Joint  

Numerical  Weather  Prediction  Unit  in  Washington,  D.C.,  researchers  developed  “objective  

analysis  procedures,”  or  machine  outputs,  based  on  numerical  weather  prediction  

methods.  Together,  these  generated  a  prognosis  of  the  synoptic  scale,  or  large  scale  

features  of  the  atmosphere,  which  statistical  techniques  then  transformed  into  guidance  for  

forecasts.66    Generated  at  a  central  location,  the  National  Meteorological  Center  in  Suitland,  

Maryland,  prognosis  maps  based  on  computer  model  solutions  were  sent  over  teletype  and  

DIFAX  to  one  of  254  local  weather  service  forecast  offices  and  weather  service  offices  

66  Fawcett,  “Six  Years  of  Operational  Numerical  Weather  Prediction”;  Klein,  “The  Computer’s  Role  in  Weather  Forecasting”;  Schuman,  “History  of  Numerical  Weather  Predication  Ad  the  National  Meteorological  Center.”  For  a  more  complete  history  of  Numerical  Weather  Prediction  and  computer  modeling  in  weather  forecasting  see  Edwards,  “Representing  the  Global  Atmosphere:  Computer  Models,  Data  and  Knowledge  about  Climate  Change.”  and  Lynch,  “The  Origins  of  Computer  Weather  Prediction  and  Climate  Modeling.”  

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across  the  United  States.  67  Within  their  local  communities,  forecasters  could  then  use  this  

guidance  to  assist  them  in  making  more  specific  predictions  for  their  respective  areas,  

mainly  about  elements  like  temperature,  cloud  cover,  wind  speed,  and  precipitation.  

  Because  of  the  speed  and  power  of  the  computer,  these  computations  could  be  

carried  out  at  a  rate  that  outpaced  the  unfolding  of  weather  itself.  For  forecasters,  it  did  so  

on  a  scale  of  resolution  and  across  specific  enough  variables  that  they  could  issue  

predictions  for  a  specific  location  in  time  and  for  particular  elements  of  the  weather  with  

increasing  accuracy.    This  allowed  them  to  meet  the  “presumed  goal  of  each  forecaster,”  

one  meteorologist  wrote  at  the  time,  “to  maximize  his  or  her  gain  over  the  climatological  

forecast,”68  or  what  they  called  skill.69  For  example,  by  1961,  these  techniques  and  special  

models  had  increased  the  overall  skill  for  five-­‐day  temperature  forecasts.  By  the  end  of  the  

decade,  computer  models  had  became  so  ubiquitous  in  their  practice  that  forecasters  gave  

a  name  to  their  relationship  with  it,  one  that  clearly  referenced  an  image  of  forecasting  

science  through  the  interplay  of  humans  and  computers.    

  The  concept  of  the  “man-­‐machine  mix,”  as  it  was  called,  first  appeared  in  a  U.  S.  

Weather  Bureau70  technical  memo  published  in  August  1969  by  Leonard  Snellman,  then  

67  Schuman,  “History  of  Numerical  Weather  Predication  Ad  the  National  Meteorological  Center”;  Friday,  “The  Modernization  and  Associated  Restructuring  of  the  National  Weather  Service:  An  Overview.”  68  Bosart,  “SUNYA  Experimental  Results  in  Forecasting  Daily  Temperature  and  Precipitation,”  1013.  69  Sanders,  “Skill  in  Forecasting  Daily  Temperature  and  Precipitation:  Some  Experimental  Results,”  1172.  70  The  Weather  Bureau  would  become  the  National  Weather  Service  in  1970.  For  a  history  of  the  institution  see  Whitnah,  A  History  of  the  United  States  Weather  Bureau.and  Hughes,  Century  of  Weather  Service:  A  History  of  the  Birth  and  Growth  of  the  National  Weather  Service  1870-­‐1970..  

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Scientific  Services  Division  Chief  at  the  Salt  Lake  City  regional  forecast  office.71  In  it,  he  

articulated  the  “roles  of  man  and  machine  in  operational  forecasting”  and  the  direction  he  

saw  for  the  field  over  the  next  decade.  It  was  an  exciting  time.    As  other  historians  have  

noted,  computer  processing  power  in  the  1950s  and  1960s  created  for  the  forecasting  

profession  an  abundance  of  data  about  the  atmosphere  and  a  range  of  options  for  digitally  

processing  and  using  this  information.72    To  aid  forecasters  in  sorting  through  their  

growing  bounty  of  information,  much  of  the  discussion  at  the  Weather  Bureau’s  

headquarters  involved  which  processes  could  be  automated  and  which  still  needed  the  

intervention  of  the  forecasters.  Such  decisions,  one  meteorologist  wrote,  depended  on  

“whether  the  amount  of  improvement  obtained  manually  warrants  the  extra  time  required  

to  modify  the  machine  product.  The  criterion  is  primarily  one  of  accuracy  versus  time…”73  

Thus,  if  a  more  precise  forecast  could  be  obtained  by  letting  forecasters  reconsider  the  

computer  model’s  output  for  a  short  period  of  time,  then  the  product  should  not  be  

automated.  Automation,  then,  offered  one  measure  of  forecaster  performance.  

  Snellman  formulated  this  relationship  as  a  mathematical  expression,  distilling  an  

evolving  relationship  into  a  quantitative  form:  (  +  Machine  =  Final  Forecast.  The  

“man”  in  parenthesis,  he  wrote,  signified  forecasters  at  both  a  “major”  center,  like  the  

National  Meteorological  Center  in  Suitland,  Maryland,  and  at  a  “local  field  station”  in  their  

community.  Working  together  as  a  team,  the  forecasters  at  the  major  center  provided  

machine,  or  computer,  guidance  to  assist  the  local  forecaster  with  daily  predictions.  

Together,  the  humans  and  the  machines  created  a  final  forecast,  or  the  “product”   71  Snellman,  “Man-­‐Machine  Mix  in  Applied  Weather  Forecasting  in  the  1970’s.”      72  Edwards,  A  Vast  Machine:  Computer  Models,  Climate  Data,  and  the  Politics  of  Global  Warming;  Fleming,  Fixing  the  Sky.      73  Klein,  “The  Computer’s  Role  in  Weather  Forecasting,”  196.  

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disseminated  to  various  users  in  various  text  and  numerical  forms.  A  daily  forecast,  for  

example,  might  call  for  partly  cloudy  skies  with  a  40%  chance  of  rain  and  a  high  of  80  

degrees.  

  Using  a  mathematical  equation  as  an  analogy  for  this  collaboration  suggests  

Snellman  intended  the  man-­‐machine  mix  to  maintain  a  certain  balance  between  the  

variables  to  the  left  and  the  outcome  on  the  right.  Together,  the  two  could  produce  

something  more  “true”  than  either  one  could  alone;  that  is,  the  sum  of  them  together  would  

produce  a  forecast  better  than  either  alone.  Yet,  it  would  be  this  very  interpretation  of  

variables,  their  ratio  and  meaning,  that  would  change  over  time.  So,  too,  would  the  product,  

at  times  reflecting  the  forecast  itself—its  quality,  speed,  accuracy,  etc.  In  other  contexts,  it  

would  reflect  the  forecasters,  as  though  the  product  of  the  man-­‐machine  mix  were  not  so  

much  the  prediction  but  the  profession.      

  This  was  true  at  the  national  level,  as  well.  A  few  months  later  in  October  1969,  

another  meteorologist  wrote  in  a  similar  vein  about  the  concept  of  the  man-­‐machine  mix.  

William  Klein,  of  the  Techniques  Development  Laboratory  in  Suitland,  Maryland,  noted  in  

his  article  that  the  “philosophy  of  the  man-­‐machine  mix  dominated”  much  of  the  process  at  

the  National  Meteorological  Center.  “This  means,”  he  wrote,  “that  certain  computer  

forecasts…  are  transmitted  directly  over  facsimile  and  teletype”  based  solely  on  machines,  

while  others  that  “draw  on  the  man-­‐machine  mix  …  are  first  modified  or  “massaged”  by  

experienced  forecasters  at  NMC  before  being  issued  to  the  field.”74    Being  generated  “solely  

by  the  machines”  was  another  way  of  saying  these  forecasts  here  had  been  automated,  with  

some  oversight  from  the  forecaster.  The  man-­‐machine  mix,  however,  relied  on  the  

74  Ibid.  

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forecaster  to  add  value  to  the  computer  model  guidance,  thus  improving  it.  Few  products  at  

this  time  were  fully  automated,  though  Klein  felt  this  would  soon  change.    

  Snellman’s  vision  of  the  man-­‐machine  mix  retained  the  value  of  the  forecaster  as  an  

arbiter  of  information  coming  from  the  machine  who  had  primary  responsibility  for  the  

needs  of  others.  In  the  late  1960s,  local  operational  offices  used  two  machine  products  

generated  by  computer  models.  The  first  was  “a  prognostic  product”  that  forecasters  could  

use  as  guidance  that  they  could  then  “adapt”  in  ways  that  addressed  the  “versatile  

requirements”  of  different  local  users.75  A  forecast  not  usable  by  those  it  was  intended  to  

inform,  then,  was  deemed  ineffective.  Thus  one  identity  of  the  forecaster  in  the  initial  intent  

of  this  concept  is  one  of  public  servant,  someone  who  must  learn  about  their  users  in  order  

to  provide  them  the  “best  and  most  appropriate  forecasts.”76  Keeping  a  balance  between  

the  man  and  machine  working  together,  he  suggested,  offered  the  promise  of  an  accurate  

and  useful  science.  

  Timeliness  also  emerged  as  a  benefit  of  computer  models  that  would  be  attributed  

to  the  profession.  An  accurate  forecast,  after  all,  is  useless  to  users  if  delivered  too  late.  

Speed  emerged  as  a  advantage  in  the  second  product  offered  by  the  machine  which  were  

“ready  for  direct  use”  to  a  particular  user,  such  as  pilots  in  aviation,  who  could  not  wait  for  

forecasts  to  be  changed  by  the  men  in  the  office.77  Forecasters  simply  “communicated”  this  

information,  by  which  Snellman  meant  simple  information  transfer,  leaving  them   75  Snellman,  “Man-­‐Machine  Mix  in  Applied  Weather  Forecasting  in  the  1970’s,”  3–5.  76  Murphy,  “What  Is  a  Good  Forecast?  An  Essay  on  the  Nature  of  Goodness  in  Weather  Forecasting,”  282.  77  In  one  of  only  a  few  surveys  conducted  on  gender  in  meteorology,  a  1982  study  found  that  10%  of  bachelor  degrees  in  meteorology  at  the  time  were  awarded  to  women.  In  1974,  of  1,554  NWS  employees,  20  were  women,  or  1.3%  of  the  workforce.  By  1980  that  number  had  change  to  39  of  1,338,  or  3.7%  of  the  workforce.  See  LeMone  and  Waukau,  “Women  in  Meteorology.”  for  more  information.  

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functioning  as  an  intermediary  between  the  machine  and  the  user.  The  man-­‐machine  mix  

in  this  example  foregrounds  benefits  of  the  machine,  namely  speed,  over  human  ability,  

though  forecasters  still  oversaw  and  thus  bore  responsibility  for  the  process.  In  this  sense,  

machines  helped  forecasters  confirm  their  own  conclusions  about  future  weather  and  did  

so  more  efficiently  since  they  could  process  more  information  more  quickly.  

   The  man-­‐machine  mix  thus  embodied  the  promise  of  a  better  science  and  an  image  

of  the  forecasters  as  legitimate  scientists  and  servants  to  society.  As  one  meteorologist  

would  later  write  of  the  advantages  of  the  machine:  “For  the  first  time,  meteorologists  

could  envision  their  science  taking  its  place  alongside  certain  select  branches  of  physics  as  

a  ‘hard’  predictive  science.”78  Why  did  they  not  consider  themselves  to  be  true  scientists  

before  the  man-­‐machine  mix?    

  Until  the  machines,  Snellman  explained,  forecasting  had  been  a  “laborious”  and  

“subjective”  process  conducted  by  a  single  individual  who  had  to  assimilate  available  

meteorological  observations  on  hand-­‐drawn  synoptic  maps,  which  offered  a  static  picture  

of  the  current  state  of  the  atmosphere.  Sometimes  referred  to  as  the  “art  of  forecasting,”  a  

term  that  still  frequents  weather  forecasting  discourse,  many  considered  this  earlier  

process  unscientific  guesswork,  in  part,  because  of  its  inaccuracy  and  slowness.  In  this  pre-­‐

computer  era,  the  “art  of  forecasting”  created  a  foil  to  the  “scientific  forecasting”79  of  later  

years,  a  bifurcation  that  has  roots  in  the  19th  century  when  meteorologists  debated  the  

appropriate  work  of  their  burgeoning  science.  As  Katherine  Anderson  argues  in  her  book  

Predicting  the  Weather,  many  meteorologists  believed  observations  and  analysis,  and  not  

prognostication,  should  be  the  focus  of  their  enterprise.  The  latter,  many  feared,  would  cast   78  Doswell  III,  “The  Human  Element  in  Weather  Forecasting,”  8.  79  Schaefer,  “Severe  Thunderstorm  Forecasting:  A  Historical  Perspective,”  164.  

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doubt  on  their  position  as  scientists  since  they  based  their  forecasts  on  subjective,  meaning  

wholly  human,  analysis,  which  was  often  wrong.80      

  Many  of  the  processes  and  practices  by  which  forecasters  arrived  at  their  prediction,  

the  how  of  forecasting,  likewise  changed  in  the  man-­‐machine  mix.    With  computer  models  

came  the  possibility  of  two  kinds  of  techniques:  subjective  (human)  versus  objective  

(machine)  forecasting.  Much  of  the  literature  published  during  the  1960s  and  1970s  

highlights  the  creation  of  an  “objective”  forecast  guidance  that  might  complement,  offset,  

and  even  replace  the  “subjective”  human  processes.  For  example,  in  their  summary  of  

1970s  weather  forecast  verification,  Nap  et  al81  note  the  problematic  as  a  central  feature  of  

forecasting  to  be  overcome:  “Although  objective  tools  are  used  [in  forecasting  methods],  

the  final  forecast  is  subjective  rather  than  objective”  since  the  human  still  has  the  choice  to  

base  their  forecast  on  the  machine  guidance  or  not.  The  consequences  of  subjectivity,  the  

authors  argued,  were  great.  “This  means  that  a  forecast  made  by  A  will  not  be  exactly  

reproduced  by  an  independent  forecaster  B  and  in  many  cases  it  is  difficult  to  describe  how  

the  forecast  is  made.”82  Humans,  that  is,  create  forecasts  that  are  unreliable  and  thus  

potentially  invalid.    

  Subjectivity  can  be  seen  as  a  professional  failure  of  a  kind  of  “aperspectival”  

accuracy,83  a  reliability  that  sits  at  the  heart  of  forecasters’  longstanding  concern  over  their  

profession.  From  an  agency  perspective,  “taming  of  human  subjectivity”84  orders  the  world  

for  both  investigation  and  for  administration.    From  a  forecaster  perspective,  the  machine  

80  Anderson,  Predicting  the  Weather:  Victorians  and  the  Science  of  Meteorology.  81  “A  Verification  of  Monthly  Weather  Forecasts  in  the  Seventies.”  82  Ibid.,  306.  83  Daston,  “Objectivity  and  the  Escape  from  Perspective”;  Daston  and  Galison,  Objectivity.  84  Porter,  Trust  in  Numbers:  The  Pursuit  of  Objectivity  in  Science  and  Public  Life,  21.  

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in  the  mix  offered  forecasters  a  language  to  describe  a  scientific  persona  that  then  linked  

other  principles  of  science—validity,  reliability,  objectivity—with  their  enterprise.  In  

optimistic  tones,  both  Klein  and  Snellman  agreed  on  the  short-­‐term  future  outcome  of  

forecasting  in  light  of  the  man-­‐machine  mix.  They  envisioned  a  time  not  long  in  the  future  

when  forecasters  might  be  eliminated  from  some  of  the  more  laborious  and  routine  aspects  

of  prediction.  Yet  in  the  long  run,  they  differed  substantially  both  on  the  man-­‐machine  mix  

and  the  ideal  of  their  science.    

  Snellman  believed  that  computer  models  would  eliminate  the  need  for  forecasters  

to  create  guidance  products  at  a  national  center,  but  the  local  forecaster  would  “remain  

paramount”  in  “adapting  it  to  meet  local  area  user  requirements,”  especially  in  the  

preparation  of  near  term,  specialized  forecasts.  He  wrote,  “This  service-­‐oriented  role  of  the  

meteorologist—if  he  is  trained  both  psychologically  and  academically  for  this  job—should  

be  challenging  and  rewarding.”85  In  his  model,  emphasis  on  the  users,  or  those  who  receive  

the  forecasts  and  act  on  forecasts  and  warnings,  takes  a  central  position  in  the  profession  

and  contributes  to  the  legitimacy  of  operational  meteorology  as  a  fully  fledged  science.    

  Snellman  saw  humans  and  machines  as  entangled  in  ways  that  suggested  they  could  

not  be  separated  without  harming  their  science.  Even  in  this  earliest  of  valences  of  the  

man-­‐machine  mix,  one  possible  forecaster  identity  is  both  a  scientific  expert  capable  of  

accurate  and  timely  predictions  and  a  public  servant  vested  in  the  local  situatedness  of  

individuals  dependent  on  their  expertise  and  advice.  Accuracy  and  service—being  right  

about  predictions  but  also  being  concerned  about  people  and  their  needs—together  are  

85  “Man-­‐Machine  Mix  in  Applied  Weather  Forecasting  in  the  1970’s,”  7.  

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entangled  and  constitute  a  good  a  forecast  and  thus  a  good  forecaster.”86  Humans  could  not  

be  eliminated  from  the  mix,  nor  could  they  be  automated  out  of  the  image  of  their  

profession.    

  Klein  saw  things  differently.  He  conceptualized  the  subjective  nature  of  human  

forecasting  as  an  obstacle  to  the  fulfillment  of  forecasting  science.  He  wrote  that  in  the  

short  term,  “the  concept  of  the  ‘man-­‐machine  mix’  will  probably  predominate  at  local  

forecast  offices”  allowing  humans  to  participate  as  an  important  element  of  forecasting.  

This  was  only  true,  however,  “until  the  decade  of  the  1990’s”  when  he  suspected  models  

would  outperform  humans  in  both  accuracy  and  speed.  “By  the  turn  of  the  century,”  he  

wrote,  “all  aspects  of  weather  forecasting  should  be  automated,  and  the  long  evolution  

from  subjective  to  objective  forecasting  will  be  completed.”87  This  more  purified  vision  of  

forecasting  science  saw  humans  as  the  source  of  bias  and  error,  flaws  that  might  keep  their  

profession  from  realizing  its  ultimate  goal  of  perfect  forecasts.    

  The  teleology  of  Klein’s  normative  vision  for  forecasting  presented  a  starker  future  

for  forecasters.  Humans  might  still  be  involved  in  the  design  of  the  machines  and  writing  of  

the  algorithms,  or  humans  might  still  oversee  the  machines  as  they  produced  their  

products,  but  they  would  not  be  scientists.  Klein  does  not  propose  an  alternative  for  

forecasters  as  experts;  instead  he  suggests  a  vision  of  forecasting  science  in  which  forecasts  

are  completely  automated  and  humans  are  disappeared.  In  effect,  forecasters  are  happily  

automated  out  of  their  jobs  because  their  science  is  better  executed  by  machines.  At  the  

moment  of  its  origination  the  language  of  the  man-­‐machine  mix  hinted  at  the  multiplicity  of  

possible  futures  for  the  identity  of  the  forecaster.   86  Ibid.,  6.  87  Klein,  “The  Computer’s  Role  in  Weather  Forecasting,”  202.  

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  Regardless  of  the  unsettled  nature  of  forecasters  in  the  future,  the  consensus  at  the  

time  framed  the  man-­‐machine  mix,  and  their  science,  optimistically.  In  1978,  George  

Cressman,  former  director  for  the  Joint  Numerical  Weather  Prediction  Unit,  which  created  

the  first  computer  models  used  in  prediction,  noted,  “Weather  forecasting  is  coming  of  age  

and  achieves  the  solution  of  problems  every  bit  as  difficult  as  those  solved  by  other  

scientific  and  engineering  professions.”88  The  machines  had  helped  propel  weather  

prediction  into  the  ranks  scientific  authority,  making  possible  a  profession  that  had  once  

been  called  weather  prophecy.89    In  fact,  the  1970s  represented  a  dramatic  transformation  

of  weather  forecasting  as  a  scientific  endeavor  for  those  in  its  employ.      

  Earl  Drossler,  then  Commissioner  for  the  American  Meteorological  Society’s  

Scientific  and  Technological  Activities  Commission,  published  a  retrospective  analysis  of  

the  events  and  activities  of  note  during  the  1970s  in  which  he  highlighted  several  

contributing  factors  to  their  growing  success  as  a  profession.    Operational  forecasters,  like  

those  in  the  National  Weather  Service,  had  arisen  from  relative  obscurity  in  the  pages  of  

prestigious  research  journals,  such  as  the  Bulletin  of  the  American  Meteorological  Society,  

one  of  the  most  respected  in  its  field  even  today.90  He  explained  that  during  the  1960s,  

fewer  than  a  dozen  articles  appeared  about  weather  forecasting  and  even  fewer  were  

written  by  operational  meteorologists.  By  1978  that  number  had  tripled  to  34—“an  

average  of  almost  three  articles  per  issue.”    Additional  signs  of  professional  success  

emerged.  Meteorological  conferences  and  workshops  began  to  offer  forecasters  a  platform  

to  discuss  issues  relevant  to  their  profession,  and  with  a  renewed  sense  of  purpose  and   88  Droessler,  “The  Weather  Forecaster  Today,”  195.  89  Anderson,  Predicting  the  Weather:  Victorians  and  the  Science  of  Meteorology.  90  Impact  factor  of  BAMS,  7.929,  according  to  the  journal  website  found  at  the  following  url:  http://journals.ametsoc.org/toc/bams/current.  

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growing  success  with  forecasts  for  major  national  weather  events,  forecasters  formed  their  

own  professional  society,  the  National  Weather  Association,  in  1975.91  It  seemed  the  

science  of  forecasting  had  begun  to  hit  its  stride.  

  Yet,  almost  as  soon  as  they  made  their  hopeful  pronouncements,  others  began  to  see  

problems  in  the  man-­‐machine  mix  that  might  harm  their  expertise.  Snellman’s  and  Klein’s  

optimism  about  the  future  of  their  predictive  enterprise  would  be  challenged  by  the  

practices  of  forecasters  themselves.  Computer  models  improved  yet  forecast  accuracy  

declined.  Why?  The  man-­‐machine  mix  was  not  just  about  the  two  entities—human  and  

nonhuman—working  in  tandem;  they  had  a  context,  a  social  import,  and  a  growing  ethical  

complexity.  This  can  best  seen  in  the  ways  the  connotation  of  the  man-­‐machine  mix  would  

change,  shifting  from  a  primarily  optimistic  and  forward  looking  language  of  the  forecaster  

to  one  of  anxiety  and  fear  over  a  potential  loss  of  their  profession.  

  Man-­‐Machine  Mix  as  Disease:  Meteorological  Cancer    

  By  the  early  1970s,  the  meteorological  community  had  begun  to  sound  an  alarm.  

Although  there  had  been  significant  improvements  on  numerical  models  and  statistical  

methods,  forecasters  did  not  continue  to  see  commensurate  improvements  in  their  daily  

predictions.  A  National  Science  Board  report  in  1972  noted  that  “Although  of  great  

economic  benefit,  present  day  forecasts  fall  well  short  of  perfection.”92  This  was  true  at  

almost  all  scales  of  weather  prediction:  short  range  (0-­‐24  hours),  medium  range  (1-­‐5  days),  

and  long  range  (beyond  5  days,  including  seasonal  forecasts).93  The  report  explained  the  

problem  on  many  fronts.  First  of  all,  the  numerical  models  used  by  the  National  

91  Harned,  “NWA  History.”  92  National  Science  Board,  “Patterns  and  Perspectives  in  Environmental  Science,”  93.  93  Ibid.  

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Meteorological  Center  in  their  guidance  products  were  best  at  creating  prognoses  for  the  

synoptic,  or  large-­‐scale,  motion  of  the  atmosphere.  Elements  such  as  fronts  and  jet  streams,  

for  example,  fared  better  in  the  models  than  smaller,  or  mesoscale,  features,  such  as  

precipitation.  Part  of  the  issue  was  with  the  sparse  observational  network,  an  important  

element  of  the  computer  model  in  that  observations  provided  its  initial  conditions.  Thus,  

only  more  and  denser  observations  would  offer  significant  improvements.  Further,  these  

synoptic  models  “smoothed  out  minor  irregularities”  and  thus  eliminated  mechanisms  in  

the  atmosphere,  such  as  turbulence,  that  might  be  partly  responsible  for  the  initiation  of  

storms.  Some  suggested  that  the  weather  community  should  slow  its  efforts  in  computer  

modeling  and  spend  more  time  collecting  observations  on  these  little  understood  

processes.94    

  Another  problem  involved  quantification.  Statistical  equations  interpolated  output  

from  these  numerical  models  into  possible  forecasts.  However,  the  variety  of  equations  that  

had  been  developed  over  the  years  for  different  weather  phenomena  raised  questions  

about  which  ones  were  best  and  which  should  be  the  standard  in  the  National  Weather  

Service.95  Model  Output  Statistics,  or  MOS,  were  promising  and  were  widely  used;  yet  they  

only  covered  a  few  weather  elements,  such  as  temperature  and  precipitation.96  And  other  

statistical  measures  needed  development  so  that  they  could  more  comprehensively  track  

the  skill  of  forecasters  in  making  improvements  (or  not)  over  the  years.  Based  on  these  

issues,  some  in  the  forecasting  community  worried  that  perhaps  they  had  “plateaued”  in  

94    Ramage,  “Prognosis  for  Weather  Forecasting,”  6–7.  95  Glahn,  “On  MOS  and  Perfect  Prog  for  Interpretive  Guidance”;  Klein,  “Objective  Forecasts  of  Surface  Temperature  from  One  to  Three  Days  in  Advance”;  Wassall,  “A  Study  of  the  Significance  of  Forecaster  Changes  to  MOS  Guidance.”  96  Glahn,  “Progress  in  the  Automation  of  Public  Weather  Forecasts.”  

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their  accuracy  given  the  current  state  of  the  science  and  so  were  in  need  of  a  

“breakthrough.”97      

  A  final  problem  in  the  man-­‐machine  mix  of  the  1970s  lay  in  developing  methods  for  

the  verification  of  forecasts.  While  the  forecasting  community  created  processes  and  

technologies  with  the  goal  of  achieving  improved  accuracy  and  timeliness  of  forecasts,  no  

database  existed  that  could  adequately  archive  forecasts  or  their  verified  outcomes.98  The  

profession  of  forecasting,  one  forecaster  wrote,  “is  sorely  in  need  of  a  quantitative  basis  for  

appraising  present  forecast  skill,  to  say  nothing  of  its  variation  with  element  and  with  

location…”.99  Proving  the  trends  in  forecast  skill  had  become  a  major  concern.    

  In  1973,  one  meteorology  department  had  collected  enough  data  in  at  

Massachusetts  Institute  of  Technology  over  six  years  to  analyze  forecasting  skill  of  

university  meteorologists  and  compare  their  outcomes  with  official  forecasts.  What  they  

found  shocked  them.  Their  skill  in  forecasting  showed  little  improvement  in  most  areas  

and  a  decrease  in  accuracy  in  others:  

…perhaps  the  most  striking  and  sobering  result  is  the  lack  of  systematic  increase  in  forecast  skill  over  the  last  six  years.  In  fact,  our  skill  in  precipitation  forecasting  has  shown  a  slight  downward  trend,  an  experience  which  seems  to  have  been  shared  by  forecasters  at  the  NMC.100      

Not  only  did  this  study  demonstrate  that  forecasters  struggle  with  accuracy  of  forecasts  for  

basic  elements  of  daily  prediction  (temperature  and  precipitation)  but  their  results  

97  Bosart,  “SUNYA  Experimental  Results  in  Forecasting  Daily  Temperature  and  Precipitation”;  National  Science  Board,  “Patterns  and  Perspectives  in  Environmental  Science”;  Ramage,  “Prognosis  for  Weather  Forecasting”;  Sanders,  “Skill  in  Forecasting  Daily  Temperature  and  Precipitation:  Some  Experimental  Results.”  98  Sanders,  “Trends  in  Skill  of  Daily  Forecasts  of  Temperature  and  Precipitation,  1966-­‐78.”  99  Sanders,  “Skill  in  Forecasting  Daily  Temperature  and  Precipitation:  Some  Experimental  Results,”  1177.  100  Ibid.  

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validated  a  surprising  fact  that  the  National  Meteorological  Center  likewise  suffered.  Other  

studies  confirmed  these  results.101  It  appeared  that  forecasters  had  lost  ground  they  

thought  they  had  won  in  the  battle  to  develop  an  ethic  of  accuracy.  

  What  could  account  for  this  loss?  Leonard  Snellman  had  a  theory  and  a  name  for  the  

problem.  In  1977  he  published  his  concerns  about  the  man-­‐machine  mix  in  an  article,  

“Operational  Forecasting  Using  Automated  Guidance.”  In  it,  he  cautioned  operational  

forecasters  about  a  particularly  virulent  threat  to  their  profession.  He  argued  that  many  

had  begun  to  accept  too  easily  model  solutions  and  simply  repackage  and  forward  the  

forecast  unchanged.  Snellman  worried  they  were  losing  themselves,  and  their  accuracy,  to  

the  machines:  “Forecasters,”  he  wrote,  “are  relinquishing  their  meteorological  input  into  

the  operational  product  going  to  the  user.”  Instead,  they  were  “operating  more  as  

communicators  and  less  as  meteorologists.”102  Importantly,  communication  in  this  instance  

did  not  mean  an  open  dialogue  with  users.  Instead,  he  saw  them  as  mere  information  

transfer  points.  

  In  an  image  that  accompanied  his  article,  he  illustrated  the  idea  of  communication  

(see  Figure  1).103  On  the  left  side  of  the  image  is  a  square  machine  representing  the  “NMC,”  

or  national  computer.  Out  of  a  slot  below  a  bunch  of  dials  comes  a  piece  of  paper,  meant  to  

represent  the  forecast.  A  series  of  unidirectional  arrows  show  the  paper  traveling  without  

stopping  into  one  the  hands  of  a  figure  representing  the  forecaster,  a  figure  with  his  back  to  

the  reader.  From  there,  the  paper  moves  at  the  same  pace  into  the  hands  of  another  figure  

101  Brown  and  Fawcett,  “Use  of  Numerical  Guidance  and  the  National  Weather  Service’s  National  Meteorological  Center”;  Jensen,  “A  Review  of  Federal  Meteorological  Programs  for  Fiscal  Years  1965-­‐1975.”  102  Snellman,  “Operational  Forecasting  Using  Automated  Guidance.”  103  Ibid.,  1043.  ©American  Meteorological  Society.    Used  with  permission.  

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who  faces  the  computer  at  the  other  side  of  the  image.  This  person  is  labeled  “User  

product.”  Here  the  process  stops.  The  image  suggests  that  the  forecaster  changed  nothing  

about  the  information  coming  from  the  machine.  In  fact,  it  calls  into  question  the  need  for  

that  forecaster  as  facilitator  of  the  process  in  the  first  place.  Take  away  the  forecaster  and  

you  have  an  image  of  automation,  the  machine  working  without  human  intervention  until  

the  product  is  delivered.  Here,  the  forecaster  is  simply  an  extension  of  the  machine.  

  Less  than  a  decade  before,  Snellman  had  written  optimistically  about  the  man-­‐

machine  mix  and  the  value  of  forecasters,  both  in  their  expertise  in  adjudicating  guidance  

and  in  the  way  they  tailored  this  guidance  for  their  various  users.  He  had  noted  a  certain  

pride  and  satisfaction  that  he  felt  his  colleagues  should  derive  from  such  service.  Now,  

however,  a  narrowly  conceived  form  of  communication,  mainly  as  information  transfer,  

threatened  to  overshadow  their  science  in  harmful,  even  destructive,  ways.  

 

 

Figure  1  Meteorological  cancer  of  the  man-­‐machine  mix  according  to  Snellman    

    Snellman  gave  this  kind  of  communication  a  name  that  invoked  a  terminal  disease.  

“Since  this  practice  is  increasing  slowly  with  time,”  he  wrote,  “it  can  be  called  

©American Meteorological Society. Used with permission.

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‘meteorological  cancer.’”104  Framed  by  the  metaphor  that  erodes  and  diminishes  its  victim  

on  multiple  fronts,  Snellman  invoked  as  the  source  for  his  professions’  newest  concern  an  

overreliance  on  those  same  valuable  machines  that  had  demonstrated  such  import  to  their  

success  as  predictive  experts  just  a  few  years  earlier.  By  simply  transmitting  the  machine’s  

expertise,  they  had  diminished  their  own.  The  man-­‐machine  mix,  in  effect,  weighted  too  

much  toward  the  machine;  it  had  lost  its  balance  and  both  forecasts  and  their  profession  

might  suffer  as  a  result.    

  Forecasters  like  Snellman  found  themselves  “between  a  rock  and  a  hard  place”  in  

proposing  a  solution.105  Some  believed  it  was  important  to  continue  developing  objective  

guidance  to  fully  realize  its  potential.  As  one  forecaster  wrote,  “In  my  view  we  have  every  

reason  to  be  optimistic  that  weather  forecasting  will  continue  to  advance.  To  begin  with,  

the  full  potentialities  of  numerical  weather  prediction  are  far  from  being  realized.”106    

Others  wondered  about  what  this  guidance  would  mean  for  them  as  professionals.  They  

began  to  recognize  the  potential  of  this  objective  guidance  to  “[destroy]  the  meteorologist’s  

significant  input”  in  the  process.  Just  what  ought  to  be  the  “proper  roles  of  man  and  

machine”  wasn’t  obviously  clear.107  At  the  heart  of  this  concern  lay  the  possibility  of  

automation  such  that  that  the  National  Weather  Service  would  simply  use  “machines  to  run  

machines,”  a  common  definition  of  automation  at  the  time.108    These  machines  could  

translate  the  forecast  for  different  users  and  leave  humans  to  manage  the  more  

104  Ibid.  105  Ibid.,  1036.  106  Reed,  “Bjerknes  Memorial  Lecture:  The  Development  and  Status  of  Modern  Weather  Prediction,”  398.  107  Snellman,  “Operational  Forecasting  Using  Automated  Guidance,”  1036.  108  Drucker,  “The  Promise  of  Automation,”  222.  

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bureaucratic  aspects  of  prediction—that  is,  it  would  leave  them  “over  the  loop.”109  

Snellman  feared  this  practice  was  becoming  the  norm  in  operational  settings  and  that  the  

quality  of  forecasts—and  the  value  of  the  forecaster—would  suffer  from  this  ill  calibrated  

“man-­‐machine  mix.”110    

  His  perspective  resonated  with  others  in  the  operational  community,  reinforcing  the  

notion  that  automation,  loss  of  their  profession,  and  computer  guidance  were  all  intimately  

intertwined.  One  study  about  trends  in  forecasters  skill,  for  example,  noted  a  “widespread  

concern  that  the  automated  prediction  may  replace  much  of  the  judgment  part  of  the  

forecaster’s  job.”  It  likewise  acknowledged  “Snellman’s  recent  concern  that  the  forecaster  is  

relying  too  heavily  on  the  guidance  (thus  reinforcing  the  replacement  in  a  kind  of  circular  

process).”111  Interestingly,  the  reason  for  the  replacement,  the  study  suggested,  was  not  

just  the  benign  implementation  of  technology  but  the  forecasters’  ways  of  engaging  with  

the  machines.  And  it  validated  other  work  that  pointed  out  the  declining  skill  of  forecasts.    

Did  this  deference  to  computer  models  indicate  that  forecasters  had  trouble  trusting  

themselves  as  experts?  Were  they  overwhelmed  and  so  making  choices  based  out  of  

frustration  or  lack  of  time?    One  thing  would  become  clear,  this  “cancer”  as  Snellman  called  

it,  created  other  effects  in  the  forecaster  community  as  this  “widespread  concern”  filtered  

into  research  studies,  office  culture,  and  even  testimony  given  to  Congress  about  the  role  of  

the  human  amid  such  rapid  technological  change.  

  Over  the  next  few  years  the  man-­‐machine  mix  and  threats  of  automation  created  a  

tension  for  administrators  in  explaining  the  benefits  of  a  more  efficient  system,  perhaps  at   109  LeFebvre,  Development  of  AWIPS.  110  Snellman,  “Operational  Forecasting  Using  Automated  Guidance.”  111  Sanders,  “Trends  in  Skill  of  Daily  Forecasts  of  Temperature  and  Precipitation,  1966-­‐78,”  766.      

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the  expense  of  the  workforce.  In  1977,  the  same  year  as  Snellman’s  seminal  publication,  Dr.  

George  Cressman,  director  of  the  National  Weather  Service,  gave  testimony  to  the  House  of  

Representatives  Committee  on  Science  and  Technology.  He  highlighted  for  the  panel  the  

various  kinds  of  automation  that  had  been  a  boon  to  forecasters  thus  far.  A  new  

development,  for  example,  called  computer-­‐worded  forecasts,  would  “at  the  push  of  a  

button”  generate  the  language  that  forecasters  had  traditionally  used  to  manually  describe  

their  predictions.  Instead  of  writing  their  text  based  on  individual  judgment  and  stylistic  

preferences,  forecasters  now  had  “limited  flexibility”  in  selecting  between  forecasting  

conditions—wind,  temperature,  cloud,  and  precipitation—as  well  as  the  time  period  for  the  

phenomena  and  the  order  of  the  phrasing.112  In  effect,  the  machines  would  now  speak  for  

the  forecasters.    

   Cressman  likewise  addressed  the  alarm  forecasters  had  expressed  within  the  

agency  about  the  consequences  of  improved  machines.  Their  agency’s  efforts,  he  said,  were  

 …sometimes  misunderstood  as  an  intention  on  our  part  to  replace  man  by  machine.  That  is  not  the  case.  The  idea  of  presenting  the  computer-­‐worded  forecast  to  the  forecaster  is  to  give  him  a  starting  point,  to  save  him  a  lot  of  his  preliminary  thinking,  to  let  him  really  concentrate  on  the  really  difficult  issues  at  hand,  and  give  him  a  forecast,  that  he  can  amend,  wipe  out,  or  redo  as  he  sees  fit.113    

Framed  as  an  issue  of  efficiency  that  could  bring  forecaster  skill  into  relief  as  they  attended  

to  the  more  challenging  elements  of  their  work  reintegrated  man  and  machine,  coupling  

them  to  elevate  their  profession.  But  this  framing  did  little  to  assuage  forecasters’  anxieties  

over  the  future  of  their  profession.    

  Many  felt  demoralized.  In  written  responses  from  the  National  Weather  Service  to  

questions  posed  in  Congressional  hearings  a  year  later,  administrators  noted  the  following.   112  Glahn,  “Computer  Worded  Forecasts,”  6.  113  Cressman,  Briefing  on  the  National  Oceanic  and  Atmospheric  Administration,  168.  

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“While  the  overall  morale  of  Weather  Service  field  personnel  cannot  be  said  to  be  low,  it  is  

not  particularly  high  either.”114  The  threats  of  automation  had  taken  a  toll  on  forecasters  

who  worried  about  their  jobs,  even  as  it  affected  their  motivation.  “Ironically,”  they  

admitted,  “the  increased  use  of  computer  products  and  its  threat  to  the  human  workforce  

has  tended  to  reduce  some  individuals'  incentive  to  put  out  the  best  possible  product.”115  

Rather  than  being  seen  as  an  issue  of  the  professions’  continual  effort  to  keep  separated  

what  clearly  required  both  humans  and  machines  to  be  successful,  the  issue  became  the  

forecasters  and  their  poor  reaction  to  automation.  Still,  administrators  acknowledged  that  

automation  potentially  had  negative  impacts  for  the  quality  of  their  forecasts  for  others,  

too.  “The  increased  use  of  mass  dissemination  methods  and  the  lack  of  travel  have  tended  

to  shelter  the  forecaster,  cutting  him  off  from  the  vital  feedback  as  to  users'  needs  and  the  

consequences  of  the  forecasts.”116  Mediated  communication  strained  forecaster  

interactions  with  their  users,  raising  questions  Snellman  first  addressed  in  his  1969  paper  

about  what  constitutes  a  good  forecast  and  thus  a  good  forecaster.    

  Finally,  concerns  about  being  replaced  reached  the  heads  of  the  National  Weather  

Service’s  parent  organization,  the  National  Oceanic  and  Atmospheric  Administration.    In  

1978,  Richard  Hallgren,  acting  assistant  administrator  to  NOAA,  made  a  point  of  reassuring  

Congress  of  the  value  of  the  human  in  the  mix  amid  technological  change  in  testimony  

given  to  the  House  Subcommittee  on  Transportation,  Aviation,  and  Weather.  He  discussed  

the  limited  promises  of  digital  technologies,  like  radar,  to  provide  predictions  without  

114  National  Weather  Service  Act  of  1978,  37.  115  Ibid.  116  Ibid.  

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human  intervention.    The  committee  chairman  offered  a  clarifying  remark  about  the  value  

of  automated,  digitized  technologies  in  the  forecast  and  warning  process.  Hallgren  replied:    

I  would  not  be  prepared  to  say  that  we  should  take  the  forecaster  out  of  the  loop….  I  would  take  the  view  we  automate  just  as  many  of  the  functions  [of  the  forecasting  process]  as  we  can…  I  think  there  are  a  sufficient  number  of  uncertainties  even  with  very  advanced  technology  that  one  should  not  take  him  out  of  the  loop  entirely.117      

Forecasters,  it  seemed,  provided  a  reassuring  presence  given  the  experimental  nature  of  

some  changes.  The  machines  that  provided  new  possibilities  needed  forecasters  to  watch  

over  the  machines  should  something  go  wrong.  But  just  how  the  forecaster  would  

participate  in  the  automated  loop  more  broadly,  however,  was  unclear.  Would  the  

forecaster  be  “over  the  loop,”  as  in  Snellman’s  image,  mere  managers  of  the  computer  

process,  providing  quality  control?  Or  would  they  continue  to  be  embedded  “in  the  loop”  in  

more  meaningful  ways?    Or  could  there  be  multiple  intersecting  loops:  loops  of  automation,  

of  forecaster  expertise,  of  user  interaction?  If  forecasters  were  in  the  loop  but  not  making  

predictions,  then  forecasters  had  become  something  else.  That  is,  if  the  machine  became  

the  predictors,  what  role  was  left  for  humans?  This  question  would  take  prominence  in  the  

1990s,  as  I  will  show  shortly.  

  There  was,  however,  a  potential  silver  lining  for  operational  meteorologists.  Part  of  

the  movement  to  automate  “as  much  of  the  forecasting  process  as  possible”  included  the  

development  of  a  forecaster  workstation,  called  Automated  Field  Operations  and  Services,  

or  AFOS.  First  proposed  in  1973  by  National  Weather  Service  administration,  AFOS  

functioned  as  a  communications  technology  that  would  allow  humans  to  receive  and  

transmit  information  quickly,  instead  of  using  more  outdated  and  much  slower  tools,  

117  Ibid.,  23.  

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DIFAX  and  teletype.118  The  digital  connection  between  the  computers  installed  at  the  

national  and  local  levels  also  meant  forecasters  could  speed  up  their  own  evaluation  and  

assessment  of  information  and  their  dissemination  of  forecasts  to  their  users.    The  

microcomputer,  in  effect,  allowed  them  to  be  more  computer-­‐like  in  their  speed  and  

productivity  and  enhance  their  expertise  in  diagnosing  the  weather.  It  also  made  

forecasters  and  their  machines  more  significant  in  the  warning  process:  “AFOS,”  one  

meteorologist  wrote,  “will  shorten  the  time  between  the  recognition  of  hazardous  weather  

and  the  issuance  of  warnings  to  the  general  public  to  a  minute  or  two  at  most…  [T]his  time  

compression  can  make  the  difference  between  life  and  death.”119      

  Snellman  saw  this  machine  as  a  potential  ally  to  the  forecasters,  one  that  might  

assist  them  in  rebalancing  the  mix.  It  would  situate  the  forecaster  in  the  machine  and  the  

machine  in  the  forecaster,  diminishing  the  chance  that  humans  could  be  eliminated  from  

the  mix.  Return  for  a  moment  to  that  image  of  meteorological  cancer  from  Snellman  (see  

Figure  2).120  The  bottom  half  of  the  graphic  reveals  the  possible  alternative  to  a  future  of  

irrelevance.    Here  the  same  machine,  forecaster,  and  user  appear  as  with  the  model  of  

meteorological  cancer;  however,  a  new  actor  joins  them,  a  minicomputer  that  faces  the  

forecaster.  The  machines  in  this  image  now  each  have  different  paths  they  can  choose  for  

their  information.  The  national  computer  can  send  information  to  the  local  minicomputer,  

which  then  sends  it  on  as  a  user  product  with  little  intervention.  Or  the  national  computer  

can  send  the  information  to  the  forecaster.  Important  in  this  new  graphic,  however,  is  the  

118  Lehmann,  “AFOS:  The  AFOS  Working  Environment.”  119  U.S.  Department  of  Commerce,  “Program  Development  Plan:  Automation  of  Field  Operations  and  Services,”  4.  120  Snellman,  “Operational  Forecasting  Using  Automated  Guidance,”  1043.  ©American  Meteorological  Society.    Used  with  permission.  

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choice  given  to  the  forecaster,  who  may  elect  to  use  the  local  minicomputer  to  send  

information  to  the  user  product  or  simply  pass  information  directly  to  it.  Only  the  

forecaster  decides  what  happens  to  the  information,  though  only  the  national  computer  

actually  generates  it  to  begin  with.        

  The  compromise  here  makes  both  machine  and  human  not  only  equally  important  

but  mutually  imbricated  in  a  forecast  such  that  the  user  product  is  not  delivered  without  

both  working  together.    That  is,  they  function  as  a  cyborg  assemblage.  And  as  Haraway  

notes  of  cyborg  worlds,  this  is  “about  lived  social  and  bodily  realities  in  which  people  are  

not  afraid  of  their  joint  kinship  with  animals  and  machines,  not  afraid  of  permanently  

partial  identities  and  contradictory  standpoints.”121    It  is  an  image  of  scientific  legitimacy  

that  “challenges  dualisms”  about  the  world  even  as  it  makes  unclear  “who  is  made  in  the  

relation  between  human  and  machine.”122  Perhaps  something  that  challenges  not  only  the  

boundaries  between  human  and  nonhuman  but  between  experts  and  publics,  or  as  the  

graphic  below  suggests,  between  forecasters  and  their  users.    

  But  for  the  time  being,  Snellman  believed  AFOS  kept  forecasters  on  par  with  

machines,  restoring  balance  to  the  equation  of  the  man-­‐machine  mix.  “AFOS  with  its  great  

advantages,”  he  wrote,  “mostly  the  local  minicomputer,  will  give  the  forecaster  greater  

latitude  in  using  his  meteorological  knowledge,  thereby  improving  operational  

forecasts.”123  It  allowed  them  to  individualize  their  workstations  such  that  they  could  select  

and  save  a  set  of  procedures,  or  data  screens,  unique  to  their  preferences.  The  software  

would  allow  “each  forecaster  to  store  a  set  sequence  of  alphanumerics  and  graphics  to   121  Haraway,  “A  Manifesto  for  Cyborgs:  Science,  Technology,  and  Socialist  Feminism  in  the  1980s,”  196.  122  Ibid.,  219.  123  Snellman,  “Operational  Forecasting  Using  Automated  Guidance,”  1044.  

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appear  on  screen,”  meaning  they  could  “designate  which  screens  the  products  will  appear  

on,  which  fields  will  appear  as  overlays,  and  the  order  of  display.”124  Creating  a  

workstation,  then,  allowed  the  machine  to  become  an  extension  of  forecasters—not  the  

other  way  around—reproducing  at  greater  speeds  and  in  integrated  ways  their  

preferences,  expertise,  and  assessments  of  the  weather  of  the  day.  AFOS  seemed  to  offer  a  

way  to  rebalance  man  and  machine  by  bolstering  the  forecasters’  contributions  and  helping  

them  add  value  to  computer  model  guidance  once  again.  

 

 

Figure  2  Two  forecaster  roles  as  envisioned  by  Snellman.  Top:  Meteorological  cancer  of  the  man-­‐machine  mix.  Bottom:  Rebalanced  man-­‐machine  mix  with  AFOS.    

  Although  the  concern  expressed  over  the  replacement  of  forecasters  by  computer  

models  often  appears  in  meteorological  papers  and  conference  publications  as  a  concern  

124  Lehmann,  “AFOS:  The  AFOS  Working  Environment,”  3.  

©American Meteorological Society. Used with permission.

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over  accuracy  and  employment,  it  was  also  about  the  loss  of  a  phenomenological  

experience  of  forecasting—a  loss  of  a  personal  expertise  and  scientific  profession  

meteorologists  had  struggled  to  build  for  nearly  a  century.125  Arising  from  such  

developments,  however,  came  questions  centered  on  how  to  value  of  the  human  element  in  

forecasting  within  the  man-­‐machine  mix.      

  What  Remains:  Articulating  the  Human  Element  the  Mix  

  On  October  29,  1992,  President  George  Herbert  Walker  Bush  signed  into  law  the  

“The  Weather  Service  Modernization  Act”126  This  bill  authorized  Congress  to  spend  $4.5  

billion  over  ten  years  to  produce  three  technologies  in  an  initiative  called  Modernization  

and  Associated  Restructuring  Demonstration,  or  Modernization:  the  WSR-­‐88D,  or  Doppler  

radar,  which  would  replace  the  aging  World  War  II  radar  network;  the  Automated  Surface  

Observing  System  to  measure  real-­‐time  atmospheric  conditions  at  airports  across  the  

country;  and  the  Advanced  Weather  Interactive  Processing  System  (AWIPS),  a  new  

forecaster  workstation  that  replaced  AFOS  with  an  “advanced  computer  and  

communications  system  to  help  forecasters  integrate,  visualize,  and  analyze  all  sources  of  

weather  data”127  The  last  piece  of  the  initiative  reorganized  122  local  weather  forecast  

offices  across  the  U.S.  and  re-­‐trained  forecasters  on  new  technologies  and  policies.    

125  Anderson,  Predicting  the  Weather:  Victorians  and  the  Science  of  Meteorology;  Friedman,  Appropriating  the  Weather:  Vilhelm  Bjerknes  and  the  Modern  Construction  of  a  Modern  Meteorology;  Nebeker,  Calculating  the  Weather:  Meteorology  in  the  20th  Century.  126  The  Weather  Service  Modernization  Act  of  1992.  127  National  Oceanic  and  Atmospheric  Administration,  “National  Implementation  Plan  for  the  Modernization  and  Associated  Restructuring  of  the  National  Weather  Service”;  Friday,  “The  Modernization  and  Associated  Restructuring  of  the  National  Weather  Service:  An  Overview”;  Select  Committee  on  the  National  Weather  Service,  “Committee  on  National  Weather  Service  Modernization.”  

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  Although  AFOS  had  been  implemented  in  the  late  1970s,  by  1981,  a  Comptroller  

General  report  to  Congress  noted  the  program  was  struggling.  It  was  five  years  behind  

schedule  and  $100  million  over  budget.128  Even  worse,  design  flaws  plagued  AFOS,  such  as  

an  inability  to  transmit  radar  and  satellite  data,  which  left  it  less  useful  to  local  field  offices.  

NWS  administrators  defended  their  agency  by  arguing  that  an  effort  to  create  automated  

systems  of  this  size  and  magnitude  requires  large-­‐scale  organizational  and  management  

changes,  something  the  agency  had  not  adequately  anticipated.  “The  development  of  large,  

complex  systems  that  break  frontiers  should  be  expected  to  encounter  some  problems,”  

they  wrote  in  response  to  the  inquiry.”129  The  Comptroller’s  office  agreed:    

It  should  also  be  noted  that  NWS  attempted  to  develop  one  of  the  largest  distributed  database  systems  ever  designed,  in-­‐house,  without  trained  ADP  personnel,  without  increasing  staffing  levels,  and  without  modifying  its  organizational  structure.  Given  these  constraints,  management  and  technical  problems  are  not  surprising.130      

In  the  end,  a  new  machine  would  need  to  be  developed,  one  that  would  have  the  same  goal  

of  AFOS—to  improve  forecasting  and  warning  efficiency  through  managed  automation.  But  

the  next  system  would  be  different,  too.  Importantly,  it  would  be  created  within  a  larger  

initiative  to  upgrade  several  elements  of  the  weather  warning  system;  and  it  would  be  done  

in  concert  with  the  advice  and  insight  of  the  forecasters  themselves.    

  Changes  in  Modernization  technologies  reflected  modifications  to  the  ways  

forecasters  would  work.  Instead  of  typing  out  text-­‐based  forecasts  designated  for  different  

geographic  zones,  for  example,  the  new  system,  AWIPS,  would  allow  forecasters  to  explore  

animated  graphical  representations  of  weather  features  and  overlay  them  in  ways  they  had  

128  Comptroller  General  of  the  United  States,  “Problems  Plague  National  Weather  Service  ADP  System,”  i–v.  129  Ibid.,  73.  130  Ibid.,  75.  

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never  seen  before.  This  new  software  and  hardware  system  also  enabled  forecasters  to  

visualize  the  weather  temporally  and  spatially,  and  in  real  time.  Prior  to  AWIPS  and  its  

forerunners  like  AFOS,  forecasting  technologies,  such  as  radar  and  satellite,  were  

distributed  throughout  an  office,  often  out  of  sight  of  forecaster  issuing  warnings.  This  

decentralized  process  for  generating  forecasts  and  warnings  likewise  meant  that  data  itself  

were  integrated  within  the  minds  of  individual  forecasters,  cognitively  stitched  together  

without  the  benefit  of  overlapping  temporal  and  spatial  scales  or  the  ability  to  step  

phenomena  through  time.131  AWIPS  took  advantage  of  innovations  in  graphical  computer  

displays  developed  specifically  for  forecasters  that  generated  dimensional  representations  

of  atmospheric  variables,  enabling  forecasters  to  newly  “see”  the  hazards  they  would  warn  

for.  Although  it  was  designed,  in  part,  to  help  forecasters  newly  see  representations  of  the  

weather,  it  still  required  them  to  decide  between  the  hundreds  of  types  of  data  just  which  

are  more  reliable,  believable,  and  helpful  in  a  given  weather  context.  AWIPS,  then,  

facilitated  information  overload  and  potentially  created  an  opportunity  for  meteorological  

cancer—forecasters  deferring  to  models—to  continue  to  grow.    

  Snellman  intervened  again.  He  participated  on  many  of  the  National  Academy  of  

Sciences  committee  reports  overseeing  Modernization,  helping  facilitate  the  

implementation  of  AFOS  and  after  his  retirement,  AWIPS.132  In  1982,  he  had  developed  

what  he  called  a  “Forecast  Funnel”  that  offered  a  method  of  atmospheric  diagnosis  for  

forecasters  that  enable  a  focused  understanding  of  local  weather.  Starting  with  the  

hemispheric  level  and  working  toward  the  synoptic  level,  forecasters  gradually  assessed  

131  LeFebvre,  Development  of  AWIPS.  132  MacDonald,  “Leonard  W.  Snellman,  1920-­‐1999.”  

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each  scale  of  the  atmosphere  to  better  forecast  what  he  called  “the  problem  of  the  day.”133  

His  approach  became  a  standard  in  forecasting  practice,  one  still  taught  today.  Combining  

his  diagnostic  method  with  the  efficiency  and  complexity  of  AWIPS  kept  the  forecaster  from  

falling  prey  to  meteorological  cancer,  just  as  AFOS  should  have.    

  But  AWIPS  also  embedded  forecaster  expertise  in  the  machine  much  more  explicitly.  

Developed  as  an  iterative  process  between  computer  scientists  and  meteorologists  at  the  

Program  for  Regional  Observing  and  Forecasting  Services  and  (later  called  the  Forecast  

Systems  Lab)  in  Boulder,  Colorado,  and  the  National  Weather  Service  office  in  Denver,  the  

functionality  and  graphical  interface  of  AWIPS  underwent  significant  user  testing.134  This  

allowed  forecasters  to  co-­‐design  and  co-­‐envision  how  the  “forecast  office  of  the  future”  

might  materialize  as  part  of  their  daily  practice.  Every  six  months  during  the  development  

of  the  new  workstation  and  software,  forecasters  were  invited  to  sit  side-­‐by-­‐side  with  

meteorologists  at  PROFS,  helping  them  understand  their  preferences  and  procedures.  

Among  forecasters,  AWIPS  would  resituate  forecasters  as  experts  in  the  process,  allowing  

them  to  participate  in  the  building  of  the  computer  system  that  would  work  with  them  to  

facilitate  their  skill.  It  was  a  system  tailored  to  forecasters’  knowledge,  built,  as  it  were  to  

both  capture  and  reflect  the  human.  This  would  be  a  theme  that  developed  throughout  the  

1990s  as  forecasters  reconfigured  ways  of  knowing  the  human  in  the  mix.    

  Two  main  ideas  about  the  role  and  identity  of  the  forecaster  arose  during  the  1990s  

and  share  many  of  the  same  assumptions.  The  first  argues  that  humans  and  machines  have  

133  Snellman,  “Impact  of  AFOS  on  Operational  Forecasting.”  134  National  Weather  Service  Modernization  Committee,  “An  Assessment  of  the  Advanced  Weather  Interactive  Processing  System:  Operational  Test  and  Evaluation  of  the  First  System  Build.”  

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two  different  knowledge  domains  that  complement  one  another.135  Humans  exhibit  

judgment,  which  stems  from  their  experience,  contextual  knowledge  of  the  environment  in  

which  they  work,  and  the  meaning  they  derive  from  their  assessments.  Their  decision-­‐

making  abilities  distinguish  them  from  the  machines,  which  are  able  to  produce  forecasts  

expediently  and  with  greater  accuracy  than  the  human  yet  they  still  make  mistakes.  

Because  of  their  automation  and  speed,  such  mistake  exponentially  compound  if  not  caught  

early  enough.  Thus,  many  argued,  the  two  used  together  offset  each  other’s  weaknesses  

and  provide  better  forecasts  than  any  one  alone  might.  However,  to  best  facilitate  this  

merger,  some  suggested  importing  the  human  element  into  the  machine  through  some  

mechanized,  statistical  process.  The  economist  Harvey  Stern  wrote  in  1993  that  he  

believed    

the  only  way  to  preserve  forecasters’  valuable  domain  and  contextual  knowledge  as  an  integral  component  of  the  forecasting  process,  while  simultaneously  incorporating  automated  forecasting  guidance  into  that  process,  may  therefore  be  to  utilize  a  system  that  mechanically  combines  the  automated  and  human  predictions.136      

  This  view  instrumentalizes  the  forecasters,  making  them  simply  a  part  of  the  

machine.    Not  unlike  some  efforts  discussed  in  the  context  of  automation  decades  earlier,  

one  such  industry  executive  noted  that  “the  more  we  automate  the  more  we  need  to  know  

what  makes  the  human  being  tick.”137  The  man-­‐machine  mix,  combines  the  knowledge  of  

both  together  but  within  the  system  of  the  machine,  effectively  making  forecasters  valuable   135  Stewart  et  al.,  “Analysis  of  Expert  Judgment  in  a  Hail  Forecasting  Experiment”;  Lusk  et  al.,  “Judgment  and  Decision  Making  in  Dynamic  Tasks:  The  Case  of  Forecasting  and  Microburst”;  Stewart,  Roebber,  and  Bosart,  “The  Importance  of  the  Task  in  Analyzing  Expert  Judgment.”  136  Stern,  “The  Future  Role  of  Humans  in  the  Weather  Forecasting  Process  –  to  Provide  Input  to  a  System  That  Mechanically  Integrates  Judgmental  (Human)  and  Automated  Predictions?,”  2.  137  Noble,  Forces  of  Production:  A  Social  History  of  Industrial  Automation,  39.  

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only  if  (or  until)  it  becomes  possible  to  fully  transfer  human  decision  making  into  machine  

decision  making.  Thus,  it  preserves  the  human  temporarily  and  makes  them  the  object  of  

study  in  the  interim  as  researchers  attempt  to  find  ways  to  optimize  the  man  and  machine  

to  generate  a  “lift”  in  accuracy  statistics.  More  important  than  accuracy,  some  noted  that  a  

mechanized  combination  of  human  and  machine  would  make  forecasts  more  consistent  

day  to  day  and  thus  more  valuable  to  those  arranging  their  lives  around  weather  

information.138  Even  if  not  exactly  correct,  consistent  forecasts  allow  for  the  planning  

necessary  to  manage  industries  based  on  forecasts.  A  new  dimension  of  political  economy  

emerges  in  this  arrangement,  appraising  forecasters’  knowledge  as  an  element  of  financial  

import  in  the  larger  mechanized  system.  

  The  second  view  is  much  more  generous,  though  it  bifurcates  the  ultimate  role  of  

the  forecaster  even  as  it  preserves  the  human  in  much  more  meaningful  ways.  Instead  of  

looking  for  ways  to  enhance  the  man-­‐machine  mix  itself,  some  took  the  view  of  dividing  the  

forecasting  landscape,  giving  machines  precedence  over  that  which  they  do  best:  the  day-­‐  

to-­‐day  forecasting.  Since  forecasters  struggled  to  “routinely  improve  upon  the  accuracy”139  

of  predictions,  meteorologists  looked  for  alternative  roles  they  might  play  that  were  

equally  important  and  appropriate  to  their  professional  identity.  The  first  role  suggested  by  

authors  Brooks  and  Doswell  shifted  forecasters  out  of  their  regular  operations  and  situated  

them  as  authorities  over  warnings  issued  to  protect  people.  After  all,  they  write,  “it  is  

important  to  note  that  protection  of  life  and  property  is,  in  some  sense,  the  hardest  

138  Stern,  “The  Future  Role  of  Humans  in  the  Weather  Forecasting  Process  –  to  Provide  Input  to  a  System  That  Mechanically  Integrates  Judgmental  (Human)  and  Automated  Predictions?,”  6.  139  Brooks,  Fritsch,  and  Doswell  III,  “The  Future  of  Weather  Forecasting:  The  Eras  of  Revolution  and  Reconstruction.”  

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forecast.”  To  this  end,  they  recommended  that,  because  such  events  are  more  rare  and  thus  

free  up  time  in  the  office,  they  might  consolidate  all  NWS  offices,  reducing  them  to  a  few  

regional  hubs  occupied  with  “Top  Gun”  forecasters,  highly  trained  and  skilled.  This  caliber  

of  expert,  they  argue,  needs  to  be  part  of  social  science  studies  in  order  to  understand  what  

characteristics  qualified  one  as  a  “good  forecaster.”140  Based  on  these  findings,  then,  

forecasters  would  undergo  special  training  and  an  exceptional  education,  as  well  as  the  

best  tools  and  equipment  at  their  disposal.  Importantly,  in  this  view,  the  job  of  tailoring  

daily  forecasts  for  specific  user  needs  would  be  left  to  the  private  sector—those  who  

charge  for  their  services.    Forecasters,  then,  are  experts  in  warnings.  The  computers  and  

the  private  sector  constitute  a  new  species  of  man-­‐machine  mix,  one  that  services  users  

and  preserves  the  scientific  authority  for  public  forecasters  in  the  NWS.  

  The  other  option  within  this  second  view  of  forecaster  identity  keeps  the  services  of  

knowing  user  needs  within  the  purview  of  the  public  forecaster.  In  an  influential  article  

published  by  Allan  Murphy  in  1993,  he  asked  what  constituted  a  good  forecast.  Rather  than  

simply  focus  on  accuracy  and  timeliness  as  the  main  qualities  of  “good,”  he  parsed  

goodness  into  three  types.  A  good  forecast  marries  the  forecaster’s  judgment  with  the  

prediction;  it  demonstrates  coherence  between  the  forecast  and  the  observed  weather;  and  

/  or  it  benefits  a  user,  either  economically  or  personally.141  The  latter,  Murphy  argued,  is  

the  good  forecast  that  gets  forgotten  in  the  evaluation  of  quality  and  yet  it  is  the  most  

important,  constituting  what  he  called  a  “requisite  forecast.”  He  wrote  that  it  should  

“contain  all  of  the  information  that  potential  users  require  to  act  optimally  in  the  context  of  

140  Ibid.  141  Murphy,  “What  Is  a  Good  Forecast?  An  Essay  on  the  Nature  of  Goodness  in  Weather  Forecasting.”  

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their  respective  decision-­‐making  problems.142  In  fact,  it  is  this  goodness  of  forecast  that  

alone  constitutes  the  union  of  all  three  types  of  good.  It  is  an  ideal.  In  this  instance,  value  is  

not  about  the  pure  economics  of  accuracy  or  timeliness  but  the  usability  and  helpfulness  of  

the  forecast.  As  Murphy  notes,  “forecasts  have  no  intrinsic  value.  They  acquire  value  

through  their  ability  to  influence  the  decisions  made  by  users  of  the  forecasts.”143  A  

forecast  focused  on  accuracy  or  timelines  alone  has  no  value;  it  must  contain  concern  for  

the  user  to  be  “good”—that  is,  it  must  contain  a  multiplicity  of  ethics,  not  just  that  of  

accuracy.    

  This  view  accords  with  the  one  Snellman  first  proposed—before  meteorological  

cancer  and  the  professions’  fixation  on  competition  with  the  machines  or  with  ever  

increasing  accuracy  as  the  goal  of  the  final  forecast.  A  man-­‐machine  mix  ought  to  include  

both  a  commitment  to  accuracy  and  a  concern  for  the  users  and  their  needs.  Further,  a  

forecaster—a  good  forecaster—shows  an  interest  in  and  skill  for  both.  And  like  other  

variations  in  the  meaning  of  the  man-­‐machine  mix,  this  one  continues  as  a  potential  

identity  for  forecasters  today.  Yet,  as  my  article  demonstrates,  threats  of  meteorological  

cancer  overdetermined  possible  forecaster  identities,  ones  that  may  not  entirely  reflect  

forecaster  practices  and  potentialities.    

  Fears  over  automation  also  contributed  to  a  limited  view  of  the  forecaster.  A  writer  

in  the  1960s  proposed  that  what  people  object  to  in  automation  is  not  so  much  the  loss  of  

labor  but  the  change  in  the  image  of  themselves.  “There  is  a  strong  revulsion  in  many  

people  against  admitting  the  possibility  of  machines  behaving  like  human  beings;  they  feel  

142  Ibid.,  282.  143  Ibid.,  286.  

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that  this  would  be  equal  to  admitting  that  ‘man  is  a  machine.’”144  In  one  version  of  the  

future,  forecasters  could  see  themselves  as  machines,  whether  as  an  extension  of  the  

machine  managing  them  from  “over  the  loop”  or  as  a  representative  knowledge  base  taken  

separated  from  their  bodies  and  mechanically  integrated  with  the  machines.  It  is  an  image  

they  have  struggled  against  for  nearly  forty  years  in  their  publications  and  practices.  As  

more  than  one  forecaster  has  revealed  to  me,  even  today,  many  refuse  to  “populate  the  

[forecast]  grids”  with  computer  model  ensembles,  preferring  instead  to  work  out  the  

forecast  for  every  hour  and  every  day  for  which  they  are  responsible  on  shift.  “They’re  still  

trying  to  make  it  their  forecast,”  one  NWS  manager  noted.    

  Concerns  over  automation  coupled  with  those  over  meteorological  cancer  

projected—and  continue  to  project—a  dominant  image  of  the  future  in  which  forecasters  

with  all  their  complexity  and  expertise  become  irrelevant.  What  accounts  for  the  staying  

power  of  such  anxieties?  In  part,  the  sociotechnical  practices  and  developments  of  

forecasting  have  continued  to  shape  and  be  shaped  by  the  discourses  of  the  man-­‐machine  

mix.  For  as  much  as  this  language  tells  forecasters  what  they  ought  to  be,  so,  too,  does  it  

point  to  what  their  technologies  ought  to  be,  as  well.    

  A  related  way  to  say  this  is  that  there  is  also  the  power  of  the  words  themselves.  

Evelyn  Fox  Keller  and  Elisabeth  Anne  Lloyd  argue  that,  “Words,  even  technical  terms,  have  

insidious  ways  of  traversing  the  boundaries  of  particular  theories,  of  historical  periods,  and  

of  disciplines—in  the  process  contaminating  the  very  notion  of  a  pure  culture.  They  serve  

as  conduits  for  unacknowledged,  unbidden,  and  often  unwelcome  traffic  between  

144  Gabor,  “Inventing  the  Future,”  142.  

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worlds.”145  The  worlds  of  automation,  disease,  and  weather  prediction  illustrate  the  

movement  and  mutual  shaping  of  these  worlds  with  the  man-­‐machine  mix  as  a  point  of  

contact  for  all  three.  The  language  and  the  work  together  imbricate  the  knowledge  

production  of  meteorology  and  the  identity  of  the  forecaster.    Keller  and  Lloyd  argue  that  

such  word,  “  they  have  force  and  leave  traces:  

Upon  examination,  their  multiple  shadows  and  memories  can  be  seen  to  perform  real  conceptual  work,  in  science  as  in  ordinary  language.  They  help  to  keep  worldviews  together,  to  bridge  disparate  (even  contradictory)  concepts,  to  insulate  us  from  problems  we  cannot  solve.146      

The  problem  to  solve  here  is  what  a  forecaster  ought  to  be  and  how  to  find  possible  ways  

forward  that  reflect  the  best  of  who  they  already  are.    

  Perhaps  there  is  something  to  be  gained  here  from  the  literatures  that  mine  the  

complexity  of  automation.  One  outcome  of  this  self-­‐directed  activity,  some  have  argued,  is  

that  it  freed  many  workers  from  the  “burdens  of  production,”  forcing  them  to  reconsider  

their  place  in  the  world.  As  Supreme  Court  Justice  Arthur  Goldberg  asked  in  1962  about  

where  automation  leaves  us  as  laborers:  “But  are  we,  as  a  people,  prepared  to  turn  the  

leisure  time  we  gain  to  constructive  use,  to  recreation  in  its  true  meaning,  the  “re-­‐creation”  

of  our  lives?”147  What  does  it  take  to  re-­‐birth  a  profession,  to  transform  problematic  

identities  of  a  group  of  people  to  something  more  in  line  with  the  multiplicity  of  their  

obligations?  Recent  efforts  in  the  National  Weather  Service  to  “evolve”  their  underlying  

philosophy  from  one  of  accuracy  to  one  of  ‘deep  relationships’  with  others  may  offer  a  

beginning.  Anxiety  is  high  as  forecasters  still  grapple  with  notions  of  automation  and  

145  Keller  and  Lloyd,  “Introduction.”  146  Ibid.,  2.  147  Goldberg,  “The  Challenge  of  ‘Industrial  Revolution  II,’”  7.  

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meteorological  cancer  amid  new  paradigms  for  their  warning  practices.148  The  man-­‐

machine  mix  is  central  to  these  discussions.    

  There  might  also  be  hope  in  the  metaphor  of  meteorological  cancer.  It  could  be  re-­‐

visited  as  an  indication  of  new  kind  of  forecasting  profession.  Cancer,  as  is  commonly  

understood,  is  an  abnormal  growth  of  cells.  The  cancer  introduces  an  imbalance  into  the  

body,  with  cells  multiplying  in  ways  characterized  as  out  of  control.  These  then  spread,  or  

metastasize  and  colonize  other  cells.    But  the  image  of  cancer  also  challenges  us  to  re-­‐see  

boundaries  of  normal  and  disease,  of  life  and  death.  And  in  this  work,  cancer  becomes  a  

sociotechnial  process.  As  Lochlain  Jain’s  work  eloquently  notes,  cancer  is  a  noun  but  it  is  

also  a  verb,  “an  adjective,  a  shout-­‐out,  indeed  a  grammar  all  its  own.”149    Perhaps  in  the  

diseased  form  of  practice  that  had  polluted  Snellman’s  vision  of  a  true  forecast,  there  is  

likewise  room  to  “speak  to—and  from  within—the  [cancerous]  complex”  of  automation.  

  Multiplicities  of  Accuracy  in  Weather  Forecasting    

  Various  permutations  of  the  man-­‐machine  mix  have  given  rise  to  visions  of  what  the  

forecaster  as  scientist  ought  to  be  just  as  it  has  generated  a  multiplicity  of  accuracies  that  

manifest  today  in  national  debates.  Whether  meteorologists  predict  daily  sun  and  rain,  

winter  storms,  tornadoes  and  flash  floods,  people  in  communities  affected  by  such  weather  

express  various  expectations  for  how  correct  predictions  will  be.150  Some  dismiss  

predictions  based  on  their  own  personal  experience  with  forecasts  that  turn  out  to  be  

148  Doswell,  “Weather  Forecasting  by  Humans-­‐-­‐Heuristics  and  Decision  Making”;  Fine,  Authors  of  the  Storm;  Pagno  et  al.,  “Automation  and  Human  Expertise  in  Operational  River  Forecasting.”  149  Lochlann,  Malignant:  How  Cancer  Becomes  Us,  223.  150  Morss,  Demuth,  and  Lazo,  “Communicating  Uncertainty  in  Weather  Forecasts:  A  Survey  of  the  U.S.  Public.”;  Lazo,  Morss,  and  Demuth,  “300  Billion  Served:  Sources,  Perceptions,  Uses,  and  Values  of  Weather  Forecasts.”  

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wrong  to  varying  degrees.  Others  expect  forecasts  to  be  precise,  with  the  prediction  

matching  the  outcome.  This  is  especially  true  when  weather  turns  dangerous  and  threatens  

lives,  compelling  forecasters  to  issue  warnings,  which  explain  the  nature,  significance,  

timing,  and  spatial  details  of  a  threat.    But  just  what  does  it  mean  to  be  “correct”  and  how  

might  this  term  differ  between  meteorologists  and  their  publics?  Evidence  in  news  media  

suggests  the  disparity  is  significant.    

  Accounts  of  storms  that  came  “without  warning”  reverberate  in  media  stories  across  

the  country  each  year.  In  2015,  for  example,  a  tornado  touched  down  in  a  small  Western  

town,  destroying  multiple  homes  but  sparing  lives.  At  a  town  hall  meeting  the  next  day,  the  

community  expressed  frustration  at  a  forecaster’s  suggestion  that  they  had  received  notice  

of  the  advancing  storm.  “What  warning?”  many  shouted.  Those  employed  by  the  National  

Weather  Service  (NWS),  who  in  the  United  States  are  responsible  for  issuing  free  public  

predictions,  found  themselves  defending  the  information  they  had  created  and  

disseminated.  Many  people  revealed  that  seeing  the  tornado  itself  move  through  their  

community  functioned  as  their  first  indication  of  a  threat,  a  challenge  to  claims  of  the  

warning’s  timeliness  and  precision.  Forecasters  would  later  learn  that  warnings  issued  that  

day  had  failed  to  travel  over  mobile  phones,  one  of  the  more  common  dissemination  

methods  for  weather  alerts,  until  hours  after  the  storm  passed.151  Still,  NWS  forecasters  

met  their  agency’s  criteria  for  internal  success,  accounted  for  through  reports  of  the  

tornado  called  in  to  their  office  as  they  issued  the  warning,  and  the  appearance  of  the  

tornado  within  the  area  bordered  by  the  geometrical  shape  of  their  warning,  the  polygon.  

So  just  how  is  the  accuracy  of  a  weather  warning  known  in  the  NWS?    

151  Based  on  my  ethnographic  work  at  a  National  Weather  Service  office.  

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  There  are  many  possible  answers,  a  few  of  which  I  classify  below  (See  Table  1).  First  

is  what  I  call  performative  accuracy,  in  which  the  warning  functions  as  a  “speech  act,”152  

which  Butler  argues,  helps  to  “define  the  identity”  of  forecasters  as  protectors  of  their  

publics.  As  illustrated  in  the  above  example,  the  warning  is  a  public  demonstration  of  the  

forecasters’  expertise,  as  evidenced  by  the  ability  of  the  warning  to  match  the  spatial,  

temporal,  and  categorical  characteristics  against  the  actual  occurrence  of  the  weather  

phenomena  for  which  a  warning  was  issued.  This  performance  is  conducted  through  a  

demonstration  of  accuracy,  which  serves  to  legitimate  for  their  public  the  forecasters  as  

good  scientists.153  The  lay  public  in  this  small  town  expected  such  straightforward  

accountings.    

  Another  type  of  performative  accuracy  involves  public  trust  in  expertise.  Research  

across  the  social  sciences  suggests  people  assume  that  they  will  receive  a  warning  over  the  

television,  for  example,  and  later  personally  experience  the  threat  or  hear  about  its  

existence  proximate  to  them  on  the  news  that  evening.154  That  is,  their  sense  of  accuracy  

imbricates  risks  associated  with  warnings  and  their  own  local  epistemologies  and  

experiences.  If  they  do  not  have  such  evidence,  these  publics  may  not  trust  future  warnings  

and  thus  may  not  act  on  them  later  on.155  Many  such  findings  about  the  lay  public  situate  

152  Butler,  Gender  Trouble:  Feminism  and  the  Subversion  of  Identity.  153  Fine, Authors of the Storm, 174.  154  Mileti  and  Fitzpatrick,  Communication  of  Public  Risk;  Mileti  and  O’Brien,  “Warnings  During  Disaster:  Normalizing  Communicated  Risk”;  Brown,  “Risk  Communication  Across  Cultures:  A  Study  of  the  Impact  of  Social  Context,  Warning  Components,  and  Receiver  Characteristics  on  the  Protective  Action  of  African  Americans”;  Donner,  “An  Integrated  Model  of  Risk  Perception  and  Protective  Action:  Public  Response  to  Tornado  Warnings”;  Schumacher,  “Multidisciplinary  Analysis  of  an  Unusual  Tornado:  Meteorology,  Climatology,  and  the  Communication  and  Interpretation  of  Warnings,”  2010.  155  Gruntfest  et  al.,  “False  Alarms  and  Close  Calls:  A  Conceptual  Model  of  Warning  Accuracy”;  Simmons  and  Sutter,  “False  Alarms,  Tornado  Warnings,  and  Tornado  

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accuracy,  then,  as  a  sociotechnical  issue  bound  mainly  by  definitional  and  communication  

challenges.    What  may  also  account  for  such  expectations  is  what  Szerszynski156  called  the  

“performative  dimension”  of  trust,  which  functions  in  two  related  directions:  “Expressions  

of  trust  in  institutions  can  be  at  once  performances  of  a  ‘trusting’  role  that  are  thrust  onto  

dependent  communities,  and  also  directive  declarations  whose  intention  it  is  to  remind  

institutions  of  their  obligations  to  live  up  to  that  trust…”  That  is,  accuracy  is  a  currency  of  

trust  exchanged  between  a  particular  public  and  the  institution  responsible  for  their  safety.  

Table  1  Types  of  Accuracy  in  Weather  Warnings  

Type     Definition  Peformative  Accuracy   Accuracy  that  defines  identity  of  forecasters  as  protectors  

and  builds  trust  with  publics  Administrative  Accuracy   Distillation  of  accuracy  to  statistical  metrics  that  allow  

bureaucratic  accounting  Disciplining  Accuracy   Measures  of  accuracy  that  have  embedded  expectations  for  

public  behavior  Expertise  Accuracy   Accuracy  attributed  to  the  expertise  and  skill  of  the  scientist    

  Second  is  what  I  call  bureaucratic  or  administrative  accuracy,  in  which  “methods  of  

measurement  are  […]  highly  rule  bound  or  officially  sanctioned.”157    Within  the  National  

Weather  Service,  accuracy  is  measured  mainly  through  statistics  that  can  gauge  forecaster  

skill  by  comparing  predictions  or  prediction  errors  (e.g.,  those  that  are  wrong)  against  

Casualties”;  Hoekstra  et  al.,  “A  Preliminary  Look  at  the  Social  Perspective  of  Warn-­‐on-­‐Forecast:  Preferred  Tornado  Warning  Lead  Time  and  the  General  Public’s  Perceptions  of  Weather  Risks”;  Ripberger  et  al.,  “False  Alarms  and  Missed  Events:  The  Impact  and  Origins  of  Perceived  Inaccuracy  in  Tornado  Warning  Systems”;  Morss  et  al.,  “Flash  Flood  Risks  and  Warning  Decisions:  A  Mental  Models  Study  of  Forecasters,  Public  Officials,  and  Media  Broadcasters  in  Boulder,  Colorado,”  2015;  Morss  et  al.,  “How  Do  People  Perceive,  Understand,  and  Anticipate  Responding  to  Flash  Flood  Risks  and  Warnings?  Results  from  a  Public  Survey  in  Boulder,  Colorado,  USA.”  156  Szerszyniski,  “Risk  and  Trust:  The  Performative  Dimension,”  250.  157  Porter,  Trust  in  Numbers:  The  Pursuit  of  Objectivity  in  Science  and  Public  Life,  5.  

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climatology.  In  gauging  the  accuracy  of  forecasts  in  hazards  that  either  occurred  or  did  not  

occur,  such  as  a  tornado,  three  equations  are  used:  Probabilities  of  Detection,  or  “how  well  

events  are  covered  by  warnings;”  Critical  Success  Indices,  or  the  percentage  of  time  a  

forecast  event  matches  an  observed  event;  and  False  Alarm  Rates,  or  the  percentage  of  time  

warnings  failed  to  verify.158  These  statistics  primarily  focus  on  the  relationship  between  

the  forecaster’s  abilities  and  their  governmental  accounting  infrastructures  that  come  

together  to  facilitate  the  NWS  as  a  “center  of  calculation.”159  Thus,  while  concerns  over  

public  safety  may  be  related  to  such  measures  in  the  judgments  and  actions  of  individual  

forecasters,  the  experiences  of  communities  affected  by  dangerous  weather  are  

disappeared  in  these  metrics.  Numbers,  as  Nicholas  Rose  notes,  turn  “a  qualitative  world  

into  information  and  [render]  it  amenable  to  control,”  allowing  "a  machinery  of  

government  to  operate."160    Accuracy,  in  this  instance,  is  a  mechanism  of  measurement  that  

ensures  the  agency  is  accountable  to  its  administrative  and  fiscal  institutions.  

  A  third  kind  of  accuracy  is  disciplining  accuracy,  or  an  accuracy  that  embeds  within  

it  an  expectation  for  how  the  general  public  ought  to  behave  when  they  receive  a  warning.  

It  is  best  exemplified  by  the  concept  of  lead  time,  or  the  minutes  of  advance  notice  between  

the  issuance  of  a  warning  and  the  moment  dangerous  weather  occurs.161  Rather  than  rely  

explicitly  on  statistics,  lead  time  necessitates  that  forecasters  verify  the  appearance  of  a  

weather  event  and  compare  it  to  the  temporal  and  spatial  extent  of  the  warning  box.  In  the  

158  Office  of  Inspections  and  Program  Evaluations,  “NWS’s  Verification  System  for  Severe  and  Hazardous  Weather  Forecasting  Needs  Modernization”;  Lasorsa,  “Verification  Statistics.”  159  Latour,  Science  in  Action:  How  to  Follow  Scientists  and  Engineers  through  Society.  160  Rose,  “Governing  by  Numbers:  Figuring  out  Democracy,”  676–77.  161  Office  of  Inspections  and  Program  Evaluations,  “NWS’s  Verification  System  for  Severe  and  Hazardous  Weather  Forecasting  Needs  Modernization.”  

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process  of  creating  a  warning,  forecasters  use  a  software  program  to  draw  a  shape,  which  

they  call  a  polygon,  around  the  representation  of  the  threat  on  a  computer  screen.  This  

gives  them  a  concrete  object  with  which  to  measure  minutes  from  identification  of  threat  

on  radar  to  its  appearance  via  reports  from  different  publics.  While  indicative  of  forecaster  

skill,  this  type  of  accuracy  is  also  reflective  of  the  relationship  between  forecasters  and  the  

assumption  about  how  people  affected  will  behave.    In  the  discourse  of  public  warnings,  for  

example,  forecasters  advocate  for  changes  to  the  system  that  might  better  encourage  

people  to  take  what  many  call  “appropriate  actions.”162  What  is  appropriate  usually  refers  

to  actions  that  lead  people  to  stay  safe  but  it  also  implies  taking  those  actions  specified  by  

experts,  such  as  sheltering  or  evacuating.  “Appropriate”  also  calls  attention  to  a  limited  set  

of  choices  that  ignore  the  situatedness  of  people’s  lives.      

  Finally,  and  most  relevant  to  my  argument,  is  what  I  call  expertise  accuracy,  or  a  

correctness  and  precision  that  exemplify  the  abilities  of  an  expert,  in  this  case,  a  scientific  

expert.  Among  the  many  elements  of  a  field  that  scholars  argue  distinguish  it  as  science  or  

non-­‐science  are  those  of  objectivity,  experimentation,  verification  of  results,  and  predictive  

accuracy.163  Meteorology  is  no  exception.  To  count  as  good  scientists,  their  discourse  

suggests,  forecasters  who  issue  warnings  should  do  so  based  on  objective  evidence  (e.g.  

detection  technologies  like  Doppler  radar)  whenever  possible  and  then  be  able  to  verify  

their  product164  in  order  to  demonstrate  accuracy,  or  in  what  might  be  called  a  “perfect  

162  Committee  on  the  Assessment  of  the  National  Weather  Service’s  and  Committee  on  the  Assessment  of  the  National  Weather  Service’s,  “Weather  Services  for  the  Nation:  Becoming  Second  to  None,”  52.  163  Popper,  “Conjectures  and  Refutations.”  164  Barnes  et  al.,  “False  Alarms  and  Close  Calls:  A  Conceptual  Model  of  Warning  Accuracy”;  Kalnay  and  Dalcher,  “Forecasting  Forecast  Skill”;  Palmer  and  Tibaldi,  “On  the  Prediction  of  Forecast  Skill”;  “Skill.”  

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warning.”165  Accuracy,  then,  is  a  necessary  condition  of  forecasting  science.  Yet,  similar  to  

scholarship  about  the  notion  of  skill,166  where  such  accuracy  resides  is  a  different  question.  

While  instruments  and  tools  assist  the  scientist  in  carrying  out  rigorous  methodologies  that  

might  lead  to  results  deemed  valid  and  impartial,  or  to  theories  they  might  test  and  verify,  

scientists  typically  see  the  human  as  the  driver  and  generator  of  knowledge  production.  

Their  mechanical  devices—radar  arrays,  computers,  workstations,  and  thermometers,  for  

example—function  as  mere  technological  assistants  in  this  enterprise.  In  the  pursuit  of  

accurate  predictions,  forecasters  imagine  themselves  as  the  expert  whose  decisions  and  

actions  inscribe  accuracy  into  the  skill  of  their  forecasts.  They  see  accuracy  as  a  function  of  

themselves  and  their  scientific  training.  Yet  they  are,  in  their  practices,  more  akin  to  

cyborgs  in  their  “leaky  distinctions”  between  human  and  machine.167    

 

Conclusion:  Return  to  the  Present  

  In  June  2016,  I  interviewed  a  director  of  the  Norman  test  bed  where  new  warning  

technologies  are  being  developed  so  that,  some  day,  they  can  be  automated.  He  pointed  out  

that  not  much  has  changed  in  terms  of  the  man-­‐machine  mix  or  the  anxieties  forecasters  

feel  as  they  come  into  the  test  bed  and  participate  in  possible  developments  of  their  

profession.  I  asked  him  about  the  likelihood  that  such  technologies  might  eliminate  the  

human  from  forecasting.  He  responded  with  skepticism:      

165  Barnes  et  al.,  “False  Alarms  and  Close  Calls:  A  Conceptual  Model  of  Warning  Accuracy,”  1144.  166  Collins,  de  Vries,  and  Bijker,  “Ways  of  Going  On:  An  Analysis  of  Skill  Applied  to  Medical  Practice”;  Collins,  “The  TEA  Set:  Tacit  Knowledge  and  Scientific  Networks”;  Polanyi,  “Tacit  Knowing.”  167  Haraway,  “A  Manifesto  for  Cyborgs:  Science,  Technology,  and  Socialist  Feminism  in  the  1980s.”  

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Number  one,  algorithms  aren’t  sufficiently  advanced  that  they  can  do  it  all.  And  number  two,  I  think  that  the  fact  is  that  humans  who  are  going  to  be  making  decisions  at  the  end  of  all  this  [work],  they’ll  want  to  know  that  a  human  has  been  a  part  of  the  [forecasting]  process.  I  just  don’t  think  [users]  react  the  same  way  if  it’s  a  completely  automated  process.      

  The  old  story  of  automation  meets  a  newer  story  of  user  expectation.  As  with  cutting  

edge  technologies  developed  in  the  late  1970s,  uncertainties  about  their  function  in  the  

context  of  automation  continue  to  provide  a  reassurance  that  forecasters  will  remain  “in  

the  loop.”  But  there  is  also  the  uncertainty  of  the  user  to  contend  with.  Rather  than  

imagining  some  forecast  factory  in  which  the  whir  of  computers  can  be  heard  echoing  

hollowly  in  a  room  empty  of  humans  as  they  generate  predictive  information,  users  want  to  

know  their  decisions  are  based  on  information  alive  with  the  pulse  of  human  judgment.168    

    The  director  continued,  optimistic  as  Snellman  about  the  future  of  the  man-­‐machine  

mix  and  the  profession:  “I  think  when  you  balance  all  that  together,  there  is  an  optimum  

mix  between  the  human  being  and  the  machine….  and  there’s  going  to  have  to  be  a  lot  of  

discussions  before  we  find  [it.].”  I  wondered  to  myself  if  in  these  discussions  any  one  ever  

suggested  that  there  might  be  other  metaphors  more  rich  and  reflective  of  the  diverse  roles  

I’d  watched  forecasters  enact  in  their  practices.  The  mix,  I  wanted  to  say,  is  an  illusive  ideal,  

one  that  continues  to  retreat  into  the  future  with  each  successive  generation.  It  resists  the  

resolution  that  forecasters  have  sought  either  in  clearly  demarcating  between  human  and  

machine.  The  separation,  the  director  reminded  me,  is  still  as  separate  as  ever.  “There’ll  be  

plenty  for  the  humans  to  do  on  that  side  of  the  equation,”  he  said,  “and  to  continue  to  do  for  

a  number  of  years  to  come.”  

168  Northwestern  Mutual,  “People  Plus  Technology.”  

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  In  this  article  I  have  traced  a  brief  genealogy  of  the  man-­‐machine  mix  and  the  ways  

it  has  shaped  forecasters’  discourse  about  their  identity  as  scientific  experts  and  their  role  

in  society.  As  an  ethic  that  governs  the  weather  prediction  discourse,  accuracy  produces  an  

image  of  forecasters  as  part  of  an  equation  that  sets  them  as  both  collaborators  and  

competitors  with  the  machines  that  facilitate  their  practices.  Threats  to  the  forecaster  

identity  come  from  within  and  without,  from  the  choices  forecasters  make  about  how  to  

engage  computer  model  information  to  the  agency’s  continual  search  for  ways  to  automate  

the  forecasting  process  in  the  name  of  efficiency.  Challenges  to  traditional  forecaster  roles,  

then,  have  reaffirmed  the  value  of  accuracy  at  the  expense  of  others,  such  as  focus  on  user  

needs—something  Snellman  and  others  believe  should  be  central  to  forecaster  identity.  As  

others  in  the  community  have  shown,  the  man-­‐machine  mix  is  only  one  alternative.  

  What  I  offer  through  this  analysis  is  a  broader  vision  of  possible  forecaster  identities  

reflective  of  multiple  ethics  in  which  the  “man-­‐machine  mix”  is  less  prominent  and  thus  

less  anxiety  producing.  As  Helen  Longino,  philosopher  of  science,  argues,  “We  should  stop  

asking  whether  social  values  play  a  role  in  science  and  ask  instead  which  values  and  whose  

values  play  a  role  and  why.”169  To  this  end,  I  ask  if  an  ethic  of  accuracy  is  the  value  that  

ought  to  play  the  most  dominant  role  in  forecasting  science  or  whether  others  that  exist  in  

forecasting  practice  but  are  largely  hidden  from  public  view  might  join  accuracy  as  part  of  

their  professional  identity  of  the  science.    Thus  I  open  up  the  possibility  for  alternative  

images  that  exist  alongside  those  of  the  man-­‐machine  mix.  Especially  important  are  those  

values,  like  care  and  concern,  that  better  mirror  what  sociologist  Phaedra  Daipha  suggests  

is  their  dual  “commitment  to  science  and  commitment  to  public  service”  which  form  “the  

169  Longino,  “How  Values  Can  Be  Good  for  Science,”  127.  

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basic  building  blocks  at  the  core  of  NWS  forecasting  identity  and  logic  of  practice”[italics  

added].170  Accuracy  of  prediction  and  protection  from  harm  are  intimately  entangled.  If  

scaled  up  and  made  visible,171  the  multiplicity  of  ethics  that  attend  to  their  role  as  public  

servants  might  better  align  their  practices  with  their  mission  to  protect  people.172  

  The  multiple  valences  of  accuracy  produce  a  social  imaginary  for  both  forecasters  

and  their  respective  publics  that,  as  STS  scholar  Shelia  Jasanoff  has  said  of  such  co-­‐

production,  “encode  not  only  visions  of  what  is  attainable  through  science  and  technology,  

but  also  of  how  life  ought,  or  ought  not,  to  be  lived.”173  Figuring  the  man-­‐machine  mix,  then  

produces  an  open  question  for  the  kind  of  society  forecasters  want  to  create.  I  hope  I  have  

shown  that  forecasters  have  a  choice  and  that  the  alternatives  already  exist  in  their  

histories,  their  genealogies,  and  their  commitments.  

 

Acknowledgements:  This  work  was  conducted  with  the  help  of  a  Graduate  Student  Visiting  

Scholar  Fellowship  from  the  National  Center  for  Atmospheric  Research  and  a  grant  from  

NOAA.  

170  Daipha,  Masters  of  Uncertainty:  Weather  Forecasters  and  the  Quest  for  Ground  Truth,  49.  171  Downey,  “What  Is  Engineering  Studies  For?  Dominant  Practices  and  Scalable  Scholarship,”  2009.  172  National  Weather  Service,  “NWS  Strategic  Planning  and  Policy.”  173  Jasanoff,  “Future  Imperfect:  Science,  Technology,  and  the  Imaginations  of  Modernity,”  6.  

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Article  2:  Matters  of  Concern    Prologue    

  In  August  2004,  I  graduated  from  Goucher  College  with  an  M.F.A.  in  creative  

nonfiction,  a  genre  that  broadly  sits  under  the  umbrella  of  creative  writing,  alongside  

poetry  and  fiction.  In  English  and  creative  writing  departments  at  various  universities,  this  

degree  used  to  be  the  studio  equivalent  of  a  Ph.D.,  a  credential  designed  to  reflect  a  

student’s  understanding  of  the  craft  of  “making”  creative  works  that,  in  theory,  others  

might  someday  study  and  enjoy  as  literature.  This  framing  exists  in  many  creative  

departments,  with  art  historians  studying  the  works  of  artists,  music  theorists  evaluating  

those  of  musicians,  and  so  on.  Since  the  early  2000s,  however,  English  programs  across  the  

country  have  begun  to  offer  a  Ph.D.  in  creative  writing,  requiring  students  to  complete  both  

a  creative  component  and  critical  component  of  their  work.  As  one  colleague  who  

completed  a  degree  in  this  kind  of  program  said  to  me,  “Doing  the  work  is  no  longer  

enough—now  they  want  us  to  justify  what  we  do,  as  well.”    

  Traditionally,  justification  of  creative  writing  comes  from  situating  one’s  own  work  

in  a  broader  context  and  theory  and  from  external  evaluation,  though  a  trade  book  

publication.  Like  all  academic  units,  creative  writing  programs  encourage  their  students  to  

seek  out  respected  publishers  through  representation  by  an  editor.  This  model,  which  has  

been  challenged  by  the  recent  world  of  digital  and  self-­‐publishing,  required  students  to  

develop  a  literary  voice  and  style  unique  to  the  genre.  Students’  talent—and  endorsements  

from  successful  mentors—helped  them  to  get  book  contracts,  the  ultimate  measure  of  

success  for  a  writer.  Literary  journals  housed  mainly  at  university  presses  likewise  offered  

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a  pathway  through  the  writing  life,  and  if  they  were  sufficiently  reputable—with  high  

rejection  rates  and  a  history  of  publishing  luminaries  in  the  genre—then  journal  

publications  might  likewise  contribute  to  academic  success.  When  writing  creative  

nonfiction  in  a  field  such  as  Science  and  Technology  Studies,  there  are  additional  

justifications  but  also  benefits  to  be  gained  from  engaging  with  the  form.  Thus,  I  examine  

how  creative  nonfiction  as  a  genre  might  contribute  to  goals  of  Science  and  Technology  

Studies  (STS)  scholarship.  

  An  interdisciplinary  field  such  as  STS  is  difficult  to  distill  into  a  brief  summary  of  its  

aims  and  purpose  without  losing  the  complexity  and  diversity  of  scholars  within  it.  Still,  

from  my  perspective,  STS  work  can  be  thought  as  asking  two  main  questions  (with  related  

sub-­‐questions)  in  the  context  of  the  sociotechnical  and  technoscientific:    

1.  What  counts,  why  and  who  decides?  To  what  ends?  

2.  How  and  in  what  ways  is  the  topic  of  inquiry  more  complex  than  commonly  

thought?  

In  terms  of  scholarship,  an  important  element  in  qualifying  these  questions  as  STS  is  the  

identity  and  motives  of  the  person  doing  the  asking  and  analysis.  So  the  two  questions  

proposed  here  exemplify  STS  if  a  third  criterion  is  met:  The  author  is  likewise  a  self-­‐

identified  STS  scholar  and  intellectually  validated.  (Not  that  others  can’t  do  STS  

scholarship.  I’m  talking  here  about  how  to  know  if  something  is  STS  scholarship  from  

within  the  confines  of  the  field  per  a  dissertation).  Let’s  apply  these  three  tenets  to  the  

following  article:  

  1.    What  counts,  why  and  who  decides?  Creative  nonfiction  can  be  justified  as  STS  

scholarship,  I  argue,  and  should  for  two  reasons.  First,  the  genre  is  already  recognized  in  

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university  circles  as  scholarship  for  disciplines  that  often  interact  with  and  have  faculty  

affiliations  with  STS  departments.  Jim  Collier  and  Bernice  Houseman  are  examples,  as  are  

Kristen  Koopman  and  myself.  Creative  writing,  science  fiction,  and  the  like  are  not  always  

STS  but  when  the  authors  are  trained  in  both,  then  their  work  should  “count”  as  STS.  

Another  reason  for  creative  nonfiction  to  count  is  that  related  hybrid  genres  already  exist  

in  STS  literature.  Consider  Bruno  Latour’s  Aramis  or  the  Love  of  Technology.  In  this  book,  

Latour  draws  on  fictional  techniques  to  invent  characters  who  tell  the  story  from  their  

point  of  view,  including  that  of  the  technology  itself.    He  aims  to  create  a  “scientification”  of  

the  story  of  Aramis,  the  personal  rapid  transit  system  in  France.  He  invokes  “a  hybrid  

genre…for  a  hybrid  task,”  he  says,  which  is  to  highlight  the  tenets  of  his  Actor  Network  

Theory  but  also  to  playfully  explore  the  historical  context  of  a  failed  technology.174  

Similarly,  his  earliest  work  Laboratory  Life  includes  a  controversial  label  of  fiction  in  the  

acknowledgements.    Other  contenders  include  Donna  Haraway’s  “Cyborg  Manifesto”  and  

Primate  Visions,  Rachel  Carson’s  Silent  Spring,  and  dozens  of  others  by  writers,  such  as  

Richard  Rhodes,  Oliver  Morton,  Diane  Ackerman,  and  Naomi  Oreskes.  While  not  necessarily  

labeled  within  STS  as  nonfiction,  these  books  cross  genres  and  are  read  in  cultural  criticism  

classes  in  English  and  creative  writing  programs.  Theoretical  frames  shared  by  STS  and  

creative  writing—feminism,  for  example,  or  deconstruction—create  a  common  genealogy  

that  ought  to  extend  to  publications.    

  To  what  ends?  Like  many  in  STS  who  wish  to  challenge  linear  models  of  knowledge  

production,  creative  nonfiction  also  offers  this  type  of  intervention.  In  STS,  this  work  has  

174    Latour,  Aramis,  or  the  Love  of  Technology,  2.  

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been  called  by  many  names:  Mode  2  and  action  research,175    high  church  and  low  church,176  

activist  or  militant  research,177  and,  more  recently,  making  and  doing.178    Thus  creative  

nonfiction  may  be  seen  as  extension  of  an  activist  agenda  in  how  it  allow  the  scholar  to  

mobilize  her  interventions  beyond  traditional  academic  journals  toward  larger  audiences.    

  Creative  nonfiction,  then,  might  also  contribute  to  STS  approaches  to  “action-­‐

oriented”  research.  By  “finding  frictions”  in  a  particular  site  and  then  describing  them  in  

such  a  way  so  as  to  reveal  possible  interventions  or  to  offer  interventions  through  the  

description,  the  scholar  can  “problematize  distinctions  between  description  and  action.”179    

Publishing  an  essay  about  the  practices  of  forecasting  that  are  often  invisible  in  a  

journalistic  publication,  for  example,  both  reveals  that  hidden  element  of  the  forecasting  

identity  and  potentially  alters  how  forecasters  think  about  themselves  or  how  the  public  

views  the  value  of  forecasters  as  public  servants.  In  this  case,  the  description  and  

arrangement  of  the  narrative  performs  the  activism.  

  Creative  nonfiction  also  functions,  at  times,  as  a  form  of  science  communication  and  

at  others  as  a  source  of  public  participation  in  science  and  technology  controversies.  It  can  

transform  the  dense  and  difficult  jargon  of  academia  into  narratives  that  illuminate  the  full  

spectrum  of  actors  engaged  in  a  particular  project—it  shows  the  messiness  and  context,  the  

175  Nowotny,  Scott,  and  Gibbons,  “‘Mode  2’  Revisited:  The  New  Production  of  Knowledge.”  176  Fuller,  “Constructing  the  High  Church-­‐Low  Church  Distinction  in  STS  Textbooks.”  177  Russell,  “Beyond  Activism/Academia:  Militant  Research  and  the  Radical  Climate  and  Climate  Justice  Movement”;  Woodhouse  et  al.,  “Science  Studies  and  Activism:  Possibilities  and  Problems  for  Reconstructivist  Agendas.”  178  Downey  and  Zuiderent-­‐Jerak,  “Making  and  Doing:  Engagement  and  Reflexive  Learning  in  STS.”  179  Zuiderent-­‐Jerak,  “Editorial  Introduction:  Unpacking  ‘Intervention’  in  Science  and  Technology  Studies,”  231.  

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motivations  and  interests  of  multiple  sides.  In  this  sense,  creative  nonfiction  is  a  form  of  

knowledge  production  that  can  help  issues  of  interest  to  STS  scholars  travel  further.    

  2.  How  and  in  what  ways  is  the  topic  of  inquiry  more  complex  than  commonly  

thought?  Although  done  through  different  means,  good  creative  nonfiction  likewise  offers  a  

“more  complex  than  that”  view  of  the  world.  Through  the  authors’  choices  of  subject  

matter,  the  connections  they  bring  together,  the  assemblages  they  create,  nonfiction  offers  

writers  an  open  genre  flexible  enough  to  account  for  the  complexities  they  find.  Like  

Lochlain  Jain’s  work  Malignant,  a  personal  and  theoretical  nonfiction  account  of  cancer,  

writing  that  crosses  nonfiction  and  STS  needs  a  trustworthy  persona  to  explore  the  rich  

imbrications  of  self  and  world,  self  and  others,  self  and  self.  In  nonfiction,  a  candidate  for  an  

STS-­‐like  work  would  be  Siddhartha  Mukherjee’s  Pulitzer  Prize  winning  book  The  Emperor  

of  All  Maladies:  A  Biography  of  Cancer.  Written  by  a  physician  who  is  fluent  in  science  

writing,  the  book  is  represented  by  a  trade  publisher,  Scribner,  and  is  taught  in  creative  

nonfiction  programs  across  the  country.  Yet  the  book  also  asks  us  to  think  about  what  

counts  as  cancer  and  reveals  a  genealogical  complexity  of  its  subject  in  ways  that  an  STS  

scholar  might.  Not  that  the  two—STS  and  nonfiction—are  the  same,  of  course,  but  there  is  

much  overlap.  

  3.  Identity  and  motivations.  Finally,  the  distinguishing  characteristic  between  

nonfiction  and  STS  seems  to  involve  to  the  author’s  motivations,  purpose,  and  training,  and  

related  to  this,  the  use  of  jargon.  I  come  from  both  worlds,  with  an  M.F.A.  in  creative  

nonfiction  and  now  a  Ph.D.  in  STS.  They  inform  one  another  in  my  work,  though  I  have  

rather  self-­‐consciously  suppressed  my  literary  roots  for  the  past  five  years  in  courses  and  

writing.  As  someone  who  hopes  to  reach  an  audience  larger  than  my  committee  or  other  

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Disasters  STS  scholars,  however,  I’ve  recommitted  to  including  in  my  dissertation  work  

that  arises  from  my  two  trainings  but  is  more  aligned  in  its  purpose  with  my  audience  who  

are  not  necessarily  STS  scholars.  In  this  regard,  I  am  performing  the  work  of  “making  and  

doing”180  or  in  the  parlance  of  nonfiction,  “good  writing.”  

  Much  as  ethnography  recreates  a  field  site,  creative  nonfiction  recreates  the  context  

and  content  of  the  world  it  illuminates.  And  it  relies  on  the  techniques  of  fiction  to  do  so—

by  recreating  scenes,  using  description,  developing  characters,  using  dialogue,  and  offering  

internal  reflection.  Creative  nonfiction  writing  propels  the  reader  through  the  text  as  

though  one  is  “there”  with  the  author,  though  information  about  what  the  reader  is  meant  

to  learn  is  not  always  explicit.  In  the  case  of  the  article  that  follows,  the  narrative  itself  

relies  on  juxtaposition,  dialogue,  selection  of  detail,  and  personal  reflection  to  build  an  

experience  of  the  reading  as  a  documentary  film  might.  The  choices  of  which  scenes  to  

include  and  when,  of  which  characters  appear  and  how  they  are  described,  and  of  how  to  

pace  the  action—these  function  as  devices  that  create  meaning  in  the  spaces  and  overlaps  

between  them.    

  Next,  I  offer  a  brief  overview  of  nonfiction  types  so  the  reader  may  understand  what  

creative  nonfiction  is  and  how  it  functions.  In  general,  there  are  three  genealogical  lineages  

for  nonfiction.  In  no  particular  order,  they  are  journalism,  autobiography,  and  essay.  In  the  

1960s,  a  group  of  writers  created  a  movement  they  called  “New  Journalism”  to  describe  a  

novel  approach  of  writing  that  married  the  techniques  and  tenacity  of  journalism  with  the  

stylistic  approaches  of  fiction.  Writers  like  Tom  Wolfe,  Truman  Capote,  Hunter  S.  

Thompson,  Norman  Mailer  and  Gay  Talese  are  examples  of  early  practitioners  of  this   180  Downey  and  Zuiderent-­‐Jerak,  “Making  and  Doing:  Engagement  and  Reflexive  Learning  in  STS.”  

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writing  form.  Today,  one  might  hear  other  iterations  of  this  genre  as  “literary  nonfiction”  or  

“narrative  journalism”  or  “long  form  nonfiction.”  Writers  who  practice  in  this  field  hold  

verity—or  accuracy  of  retelling—as  the  highest  standard.  Writers  in  this  sub-­‐genre,  then,  

spend  time  researching  their  narratives  as  a  reporter  might,  finding  multiple  accounts  that  

validate  “the  truth”  of  a  particular  event.  To  distinguish  themselves  from  journalists,  they  

tell  the  story  “slant”—that  is  from  a  unique  point  of  view  and  with  a  unique  voice  

sometimes  through  the  techniques  of  fiction,  even  if  not  fictionalizing.  For  example,  in  1965  

Gay  Talese  tried  to  set  up  an  interview  with  Frank  Sinatra  for  an  article  commissioned  by  

Esquire.  When  the  singer  turned  him  down  due  to  illness,  Talese  instead  interviewed  

people  around  Sinatra  and  observed  him  as  he  was  able.  The  resulting  work  “Frank  Sinatra  

Has  a  Cold”  is  now  a  seminal  work  in  New  Journalism,  both  in  technique  and  approach.  

  New  Journalism  centers  the  author’s  voice  in  the  narrative.  One  “hears”  the  author’s  

voice  above  all  others—she  make  sense  of  the  story,  guide  the  reader  through  facts,  

anecdotes,  descriptive  language.  As  with  other  genres  in  creative  writing,  the  authorial  

voice  and  style,  is  the  most  important  aspect  of  successful  writing.  Students  can  spend  

years  “finding  their  voice,”  honing  it  through  various  publications,  looking  for  authenticity,  

uniqueness,  even  resonance.  The  writer’s  ability  to  tell  a  good  story,  we’re  taught,  hinges  on  

this  elusive  and  hard-­‐won  attribute  of  our  writing.  The  reader  experiences  the  narrative  

through  the  writers,  the  way  the  author  arranged  the  characters,  the  setting,  the  details  

revealed  or  omitted.  So  writers  must  work  hard  to  “hook”  the  readers,  to  keep  their  

interest,  to  show  them  something  they  can’t  see  otherwise.  “Show,  don’t  tell”  is  a  hallmark  

truism  in  nonfiction.  You  must  recreate  what  you  see  and  experience.  The  writer,  then,  is  

the  instrument  of  the  “well  told.”  

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  The  much  older  genealogical  thread  of  nonfiction,  autobiography,  relies  not  only  on  

the  author’s  voice  for  success  but  the  dramatic  and  unique  nature  of  the  life  being  re-­‐told.  

According  to  British  poet  Robert  Southey,  traditionally  people  wrote  autobiographies  in  

middle  and  older  age  when  they  had  attained  a  certain  distance  and  reflection  on  their  

lives.  Many  autobiographies  gained  success  because  of  the  author’s  fame,  though  fame  was  

not  always  a  self-­‐attribution  and  not  all  books  were  published  during  the  lifetime  of  the  

author.  Ben  Franklin’s  family  published  his  autobiography  after  his  death,  for  example.  

Other  books  detailed  important  sociocultural  points  of  view:  Harriet  Jacobs  published  her  

account  of  life  as  a  slave  girl  under  a  pseudonym  since  it  exposed  the  treatment  of  slaves  in  

the  South;  Vera  Brittain’s  account  of  the  lost  generation  after  World  War  I  is  part  tribute,  

part  reflection  from  an  early  feminist  perspective.  Autobiography,  then,  is  a  “long  view”  of  a  

life,  one  situated  in  the  partial  truth  of  history.    

  A  related  genre,  memoir,  highlights  a  shorter  episode  of  one’s  life.  It  shifts  readers’  

focus  to  incidents  the  author  experienced  that  might  resonate  with  (or  shock  or  horrify)  

readers.  In  nonfiction  circles,  the  1990s  are  the  decade  of  “the  confessional  memoir,”  an  

explosion  of  books  written  by  those  who  had  been  subject  to  rape,  alcoholism,  cancer,  

abuse,  and  other  difficult  experiences.  Criticized  for  their  shock  value  and  over-­‐sharing,  

authors  in  this  genre  care  less  about  “verity”  since  personal  memory  and  time  erode  

“truth,”  they  argue.  Instead,  their  work  is  more  voyeuristic,  answering  questions  such  as,  

What  are  the  limits  of  what  can  be  shown?  In  Kathryn  Harrison’s  memoir  The  Kiss,  for  

example,  the  author  reveals  in  great  detail  the  affair  she  had  with  her  estranged  father  after  

they  were  reunited  in  her  adulthood.    

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  Still,  some  sense  of  verifiable  truth  is  important.  Should  an  author  be  caught  lying  

public  judgment  is  swift  and  merciless,  as  some  have  discovered.  James  Frey  invented  

people  who  later  died  in  his  book;  Herman  Rosenblat  invented  an  entire  experience  in  the  

concentration  camps;  and  Margaret  Jones  lied  about  her  identity—these  writers’  lies  led  to  

public  controversy  and  private  shame.  They’ve  had  contracts  voided,  books  recalled,  and  

reputations  ruined.    

  Memoir,  thus,  combines  the  concerns  of  authorial  voice  with  a  talent  for  either  

telling  a  dramatic  story  such  that  the  reader  feels  they  are  living  it  or  telling  a  story  that  is  

seen  as  having  lessons  one  can  learn.  Because  of  the  popularity  of  the  subject  and  the  

telling,  however,  memoir  is  less  “academic”  than  something  written  from  a  journalistic  

point  of  view.  It  may  bring  financial  and  popular  success  to  the  author  but  can  also  call  into  

question  the  author’s  intent,  motivation,  personal  life,  politics,  etc.  There  are  exceptions,  of  

course,  but  some  programs  steer  students  away  from  memoir  and  toward  the  personal  

essay  should  they  have  an  interest  in  investigating  their  own  interiority.  

  The  essay  is  an  amorphous  genre  with  roots  in  the  kinds  of  self-­‐reflective  writing  of  

people  like  Michel  de  Montaigne,  known  for  popularizing  the  form  in  the  16th  Century.    

Styles  vary  from  more  formal  critique,  literary  analysis,  and  argumentation  to  less  formal  

varieties  that  explore  the  author’s  ideas  and  experiences.  The  former,  according  to  essayist  

Phillip  Lopate  has  a  “seriousness  of  purpose,  dignity,  logical  organization”  more  in  keeping  

with  “factual  or  theoretical  prose  writing.”  Different  sub-­‐genres  that  have  specific  foci  and  

might  be  considered  more  formal  include  nature  writing,  travel  writing,  and  science  

writing.  However,  the  latter,  Lopate  writes,  engages    

the  personal  element  (self-­‐revelation,  individual  tastes  and  experiences,  confidential  manner),  humor,  graceful  style,  rambling  structure,  unconventionality  of  novelty  of  

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theme,  freshness  of  form,  freedom  from  stiffness  and  affectation,  incomplete  or  tentative  treatment  of  topic.181    

Personal  essay,  then,  is  a  form  that  can  be  characterized  by  fragmentation  and  nonlinearity  

such  that  it  creates  meaning  though  juxtaposition  of  images,  temporalities,  and  episodes.  

Timelines  may  not  be  linear,  transitions  may  constitute  no  more  than  a  double  space  on  the  

page  or  in  inclusion  of  this  mark  (***)  as  an  indicator  of  narrative  shift.  In  more  

experimental  forms,  the  essay  may  be  combined  with  the  conventions  of  poetry  to  create  a  

lyric  essay,  a  form  that  requires  more  work  on  the  part  of  the  reader  to  make  associations  

and  connections.  Other  experimental  forms  include  “short  short”  or  flash  nonfiction,  that  is,  

pieces  completed  in  a  paragraph  or  a  few  sentences,  and  others  may  play  with  point  of  

view,  or  second  and  third  person  perspectives.        

  A  difficult  genre  to  clarify,  the  essay  is  often  defined  by  what  it  is  not:  it  is  not  fiction  

and  it  is  not  poetry.  It  is  not  autobiography  nor  memoir.  What  it  is  carries  forward  as  

authors  situate  themselves  as  reliable  narrators  or  not,  is  a  sense  of  one  who  treats  the  

topic  fairly.  At  its  best,  personal  essay  writing  creates  an  emotional  intimacy  with  the  

reader  not  based  so  much  on  tidy  conclusions  or  direct  explanation  but  on  musings,  

imaginings,  and  a  conversational  style.  Motivated  by  questions  such  as  “What  do  I  know?”  

and  “What  will  resonate?”  Lopate  suggests  “the  struggle  for  honesty  is  central  to  the  ethos  

of  the  personal  essay.”182  Thus,  the  voice  and  the  writer’s  efforts  to  engage  the  topic  matter  

deeply.  Finally,  the  form  encourages  play  between  the  writer’s  personal  interests  (insights,  

anecdotes,  and  descriptions)  and  issues  of  concern  in  the  world.  For  some,  this  creates  a  

tension  between  the  personal  and  the  more  general,  wherein  a  specific  instance,  memory,   181  Lopate,  The  Art  of  the  Personal  Essay:  An  Anthology  from  the  Classical  Era  to  the  Present,  xxiii–xiv.  182  Ibid.,  xxv.  

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or  event  may  trigger  themes  that  move  out  beyond  the  self  to  capture  issues  of  important  

to  a  broader  audience.    

  These  forms  of  nonfiction  vary  dramatically  but  have  a  common  aim  of  being  read  

by  and  affecting  a  large  “general”  audience.  In  part,  this  aim  is  imbricated  with  the  trade  

publishing  world,  which  awards  contract  amounts  based  on  the  number  of  copies  an  editor  

believes  a  book  will  sell.  Thus,  writers  aim  for  a  large  base  of  readers—not  everyone,  but  

large  enough  to  merit,  say,  a  six-­‐figure  advance.  But  it’s  not  just  about  money.  There  is  also  

a  desire  among  most  nonfiction  writers  I  know  to  share  what  they  have  experienced  or  

have  learned  with  others.  They  are  caught  by  the  topic  of  their  book  idea—a  book  about  

extinct  birds  and  the  value  of  contemplating  extinction  to  society,  for  example.  They  can  

write  as  public  intellectuals,  transforming  their  research  into  provocative  polemics  on  race  

and  gender,  for  example.  They  ask  questions,  teach  others,  and  critically  intervene183  in  the  

worlds  they  reveal.    While  this  is  an  oversimplified  and  abbreviated  account  of  the  

nonfiction  form  as  it  was  taught  to  me,  I  hope  I’ve  given  a  sense  of  the  variations  of  

scholarship  in  creative  nonfiction.    

  My  goal  in  the  essay  that  follows  is  to  reveal  to  forecasters  how  the  ethic  of  care  

already  resides  in  and  is  mutually  reflected  in  technical  and  nontechnical  dimensions  of  

their  work,  but  this  is  something  I  won’t  say  directly.  The  other  articles  in  my  dissertation  

do  this  kind  of  analysis  in  ways  appropriate  to  the  audience.  For  my  nonfiction  work,  I  am  

allowing  the  associations  between  experts  and  publics,  or  more  precisely  between  

forecasters  and  a  particular  instantiation  of  a  public,  to  generate  meaning.  Much  as  one  

would  hope  to  watch  a  film  without  continual  narrator  voice  over,  I  have  minimized  my  

183  Downey  and  Dumit,  “Locating  and  Intervening.”  

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authorial  voice  and  am  letting  the  scenes  “speak.”  STS,  then,  is  evident  in  my  choices  of  

material,  the  alternative  image  of  care  I  help  reflect,  and  the  situatedness  of  this  article  

within  my  dissertation  more  broadly  and  my  own  education  specifically.    

  A  few  notes  about  how  to  read  this  piece.    

  The  title  itself  is  meant  to  evoke  a  sense  of  the  intent  for  this  piece.  I’m  not  explicit  in  

my  use  of  the  word  care  throughout  because  the  scenes  themselves  were  chosen  to  

demonstrate  care.  And  I  select  care  as  an  explicit  focus  of  this  work  because  care  and  

concern  remain  largely  invisible  in  the  public  image  of  the  forecaster—they  are  masked  by  

the  dominant  image  of  accuracy.  However,  I  do  come  close  to  mentioning  the  title  in  the  

introductory  section  by  referencing  “matters  of  love  and  concern,”  a  variation  on  what  I  

mean  by  care.  The  title  and  structure  also  invoke  Latour’s  article,  “Has  Critique  Run  Out  of  

Steam?”  and  his  use  of  “matters  of  concern”  to  highlight  a  direction  of  critique  as  “not  away  

but  toward  the  gathering,  the  Thing.”184  What  I  am  hoping  to  offer  in  this  bricolage  is  a  

multiplication  of  meanings,  an  opening  up  of  a  conversation,  perhaps  even  a  new  way  of  

seeing.    

   This  is  part  of  the  essay’s  work  of  showing  and  not  telling.  I  begin  the  article  “in  

medias  res,”  or  in  the  middle  of  things,  a  common  and  device  to  hook  the  reader.  I  then  use  

a  narrative  arc,  or  a  gradual  increase  in  action  and  tension  to  bring  the  reader  to  a  specific  

climactic  point  in  the  work.  Such  a  moment  is  not  meant  necessarily  to  be  more  revealing  

than  the  others,  but  it  helps  pace  the  writing,  creating  a  sense  of  drama  and  engagement.  

Throughout,  I  use  the  stylistic  devices  of  double  spaces  between  sections  that  are  still  

linked  together  to  show  a  soft  transition.  I  use  three  asterisks  (***)  to  create  hard   184  Latour,  “Why  Has  Critique  Run  Out  of  Steam?  From  Matters  of  Fact  to  Matters  of  Concern,”  246.  

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transitions  that  signal  a  potential  shift  in  tone,  subject  matter,  or  time.    Again,  these  create  

an  episodic  character  in  the  reading  akin  to  film  techniques  such  as  soft  takes  to  transition  

between  scenes  and  jump  cuts  where  the  transition  is  abrupt.  They  are  common  practice  in  

creative  nonfiction  publications.    

  To  reinforce  this  notion  of  episodes,  I  am  following  another  form  called  the  

“braided”  essay  in  which  two  or  more  narratives  alternate  in  some  pattern  throughout  the  

work.  In  this  case,  I  braid  the  story  of  Julie  with  a  patchwork  of  forecaster  experiences  and  

points  of  view.  These  are  connected  to  the  more  reflective  sections  that  introduce  concepts  

such  as  Tornado  Alley  (Oklahoma)  and  Dixie  Alley  (Alabama)  to  show  the  similarities  of  

experience  across  them.  Pauses  for  reflection  also  offer  the  reader  a  break  from  the  action,  

and  introduce  reflective  depth  that  builds  alongside  the  action.  Finally,  the  piece  ends  not  

with  a  conclusion  but  at  least  a  sense  of  resolution.  That  is,  the  denouement  of  the  piece  

offers  a  sense  of  closure  to  the  article  but  not  to  the  subject.  In  fact,  like  many  STS  works,  a  

creative  nonfiction  essay  leaves  the  reader  with  more  questions  than  answers  but  with  a  

sense  of  satisfaction  in  the  treatment  of  the  subject  in  the  context  of  the  genre  and  form.  

  Finally,  a  note  about  format.  Most  personal  essays  do  not  include  formal  in-­‐text  or  

footnote  citations.  Instead,  such  information  is  explained  in  the  appendix  or  index.  Other  

times,  a  simple  author  and  book  title  in  the  text  suffices.  In  this  regard,  the  essay  is  more  

aligned  with  its  sister  genres,  fiction  and  poetry.  Still,  for  the  purpose  of  the  dissertation,  

I’ve  included  footnotes  and  a  bibliography.    

  These  are  lofty  aspirations,  of  course,  and  will  be  judged  by  the  peer-­‐reviewed  

publications  I’ve  selected  to  submit  this  to.  I’ve  also  asked  my  M.A.  thesis  advisor  and  

creative  nonfiction  writer  Chris  Cokinos  to  review  this  manuscript  for  suggested  edits  and  

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publication  venues.  He’s  published  two  books  of  nonfiction,  two  books  of  poetry,  several  

essays  and  critical  theory  articles,  and  is  currently  an  associate  professor  of  English  at  

University  of  Arizona  where  he  is  a  mentor  in  science  communication  and  the  Carson  

Scholars  Program.    

  In  the  creative  nonfiction  publication  process,  many  literary  journals  where  such  

work  finds  a  home  offer  the  author  an  option  of  “simultaneous  submissions.”  This  allows  

writers  to  increase  their  odds  of  publication  for  one  work  while  giving  the  editors  notice  

that  if  they  want  to  publish,  they  must  make  the  decision  quickly.  Most  literary  journals  

take  between  3-­‐6  months  to  make  a  decision  and  few  ask  the  author  to  make  edits  to  their  

work  before  publication.  Thus,  there  is  rarely  a  revise  and  resubmit  process.  Acceptance  for  

publication,  then,  leads  to  a  longer  circulation.  

  Word  limits  for  creative  nonfiction  work  vary  by  journal,  especially  those  that  take  a  

variety  of  nonfiction  work.  I  have  written  this  article  at  a  length  that  gives  me  the  most  

choices—between  5,000  to  7,000  words.  Further,  the  journals  I’ve  identified  are  well  

respected  and  are  regularly  ranked  among  the  top  50  literary  journals  in  the  country.  I  

have  published  before  in  each  of  them,  though  not  for  a  while.  In  2004,  for  example,  I  was  a  

runner  up  in  Fourth  Genre  for  its  Editor’s  Prize  and  I  have  written  for  years  with  an  editor  

at  The  American  Scholar,  though  in  a  different  section  of  the  journal.  With  this  in  mind,  I’ve  

selected  the  following  journals  to  simultaneously  submit  to.  

• River  Teeth:  no  word  count.  From  their  site:  “River  Teeth  is  a  biannual  journal  

combining  the  best  of  creative  nonfiction,  including  narrative  reportage,  essays  and  

memoir,  with  critical  essays  that  examine  the  emerging  genre  and  that  explore  the  

impact  of  nonfiction  narrative  on  the  lives  of  its  writers,  subjects,  and  readers.”  

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• Fourth  Genre:  8,000  words.  From  their  site:  “Given  the  genre’s  flexibility  and  

expansiveness,  we  welcome  a  variety  of  works  ranging  from  personal  essays  and  

memoirs  to  literary  journalism  and  personal  criticism.  The  editors  invite  works  that  

are  lyrical,  self-­‐interrogative,  meditative,  and  reflective,  as  well  as  expository,  

analytical,  exploratory,  or  whimsical.  In  short,  we  encourage  submissions  across  the  

full  spectrum  of  the  genre.  

• American  Scholar:  6,000  words.  From  their  site:  “The  American  Scholar  is  a  

quarterly  magazine  of  essays,  fiction,  poetry,  and  articles  covering  public  affairs,  

literature,  science,  history,  and  culture.  Published  since  1932  for  the  general  reader  

by  the  Phi  Beta  Kappa  Society,  the  Scholar  considers  nonfiction  by  known  and  

unknown  writers,  but  unsolicited  fiction,  poetry,  and  book  reviews  are  not  accepted.  

The  magazine  accepts  fewer  than  two  percent  of  all  unsolicited  manuscripts.”  

   

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 Matters  of  Concern  [Word  count:  6375]  

 

  Julie  stood  in  the  doorway  of  the  hospital,  waiting  for  the  young  couple  in  a  car  to  

open  the  door.185    

  She  looked  up  at  the  sky,  noting  the  darkening  horizon  to  the  southwest.  What  she  

couldn’t  see  just  then  was  the  mass  of  wind  and  debris  heading  her  direction  at  40  miles  an  

hour.  Because  of  its  size,  the  tornado  would  have  been  difficult  for  any  one  of  the  13,000  

people  in  its  path  to  distinguish  clearly.186  Social  media  posts  made  by  the  National  

Weather  Service  in  Norman  warned,  “The  tornado  is  so  large  you  may  not  realize  it’s  a  

tornado.”187  At  over  a  mile  wide,  it  might  have  seemed  like  a  murky  cloud  taking  up  much  

of  the  horizon,  as  it  does  in  photographs  from  that  day.  Its  eerie  sound,  described  by  some  

as  the  whirring  of  jet  engines,  may  have  been  the  only  signal  of  its  existence.    

  “They  just  wouldn’t  get  out  of  the  car,”  Julie  explained.  She  looked  down  at  her  

hands  for  a  moment,  as  though  she  were  contemplating  the  simple  act  of  opening  a  car  

door.  She  sat  on  the  other  side  of  a  small  table,  her  white  blouse  blending  into  the  white  

walls  of  the  room,  her  dark  blue  skirt  tucked  around  her  legs.  She  looked  small  but  the  

raspy  edge  to  her  voice  made  me  think  she  was  much  tougher  than  she  looked.  

  “What  did  you  do?”  Laura  asked.    

  Julie  seemed  to  be  looking  through  us.  She  shook  her  head  and  sighed.    

  “It's  like  they  were  too  scared  to  open  the  door.  Like  they  were  frozen.  I'd  go  to  the  

window  and  yell  at  them,  ‘Get  inside!’”  She  looked  up  at  us  as  she  yelled  the  words  again   185  All  excerpts  taken  from  Robberson,  Norman  Medical  Center  Interviews.  186  Spann  et  al.,  Violent  Tornado  in  Moore,  OK.  187  National  Weather  Service,  “May  20,  2013:  Newcastle,  Moore  Tornado.”  

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and  I  tried  to  imagine  myself  as  one  of  the  people  in  that  vehicle,  too  terrified  to  move.  “But  

they  still  wouldn't  listen  and  so  I  had  to  go  out  and  open  the  door  and  practically  pull  them  

into  the  hospital.”  Weather  forecasters  who  heard  her  story  after  the  storm  called  her  

actions  heroic,  inventive,  even  miraculous.  To  Julie,  however,  she  was  doing  what  she  could  

do  given  the  circumstances.  

  Laura  and  I  had  been  sitting  in  the  room  for  almost  30  minutes  listening  to  this  

woman  with  blond  hair  and  green  eyes  recount  the  afternoon  of  May  20,  2013,  a  day  when  

a  tornado  had  destroyed  most  of  the  hospital  where  she  worked.  Laura,  then  a  social  

scientist  at  Mississippi  State,  fidgeted  in  her  chair  as  I,  a  graduate  student  at  Virginia  Tech  

University,  continued  to  take  notes  on  my  yellow-­‐lined  paper.  We’d  been  sent  to  Oklahoma  

to  interview  Julie  as  part  of  an  assessment  on  behalf  of  the  National  Weather  Service.  Our  

goal,  we  were  told  by  agency  administrators,  was  to  piece  together  the  actions  that  

different  people  took  as  their  community  faced  yet  another  EF-­‐5  tornado—their  second  of  

the  strongest  possible  such  storms  in  just  over  ten  years.    

  Other  members  of  our  group,  mostly  meteorologists,  would  spend  the  day  with  their  

colleagues  in  the  National  Weather  Service,  listening  to  them  detail  the  unfolding  of  this  

“event”—their  common  shorthand  for  disaster.  They’d  ask  about  how  the  technologies  of  

warnings  performed,  what  processes  they’d  used  to  relay  the  warning,  how  prepared  they  

felt  their  community  had  been.    Laura  and  I  would  later  take  surveys  we’d  created  to  a  

crowded  downtown  corner  and  interview  news  reporters,  emergency  managers,  and  

members  of  the  general  public,.  Our  collective  hope  was  that  by  assembling  narratives  from  

different  groups  of  people,  different  points  of  view  and  experiences,  we’d  have  a  more  

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nuanced  understanding  of  the  tornado’s  effect  on  this  community  and  what  problems  we  

might  identify  that  the  agency  could  address.    

  Although  I  had  been  sent  to  help  write  an  account  of  what  I  would  hear  and  see,  I  

didn’t  anticipate  how  people’s  stories  would  reconfigure  my  own  expectations  of  what  it  

must  be  like  not  just  to  live  with  fear  and  loss  in  light  of  such  horrible  destruction  but  to  do  

so  as  a  person  with  at  least  partial  responsibility  now  for  ensuring  that  others  have  the  best  

chance  to  survive.  And  I  would  come  to  see  that  what  we  first  think  of  as  problems  often  get  

transformed  through  happenstance  and  surprise,  challenged  by  perspective,  and  live  

alongside  what  we  call  matters  of  love  and  concern.  

 

  Oklahoma  sits  at  the  center  of  what  meteorologists  label,  Tornado  Alley,  a  cluster  of  

states  from  Colorado  to  Missouri,  South  Dakota  to  Texas.  But  Tornado  Alley  has  fluid  

borders,  on  some  maps  encompassing  more  states  than  others.188  On  one,  the  area  of  

highest  risk  shows  up  as  an  inverted  boot,  the  toe  sliding  into  Iowa  and  Minnesota.  On  

another,  the  alley  is  more  like  a  blob  of  red  in  the  center  of  the  U.S.  with  the  deepest  red  

indicating  highest  tornado  count  bleeding  toward  the  edges  of  the  Southeast  and  Midwest.  

These  images  morph  with  the  seasons,  shifting  the  “alley”  north  or  south,  east  or  west.  Like  

other  indicators  of  risk,  the  areas  most  prone  to  harm  change  depending  on  criteria  experts  

choose:  number  of  dead,  number  of  tornadoes,  population  demographics,  and  

characteristics  of  the  tornadoes  themselves.    Select  one  combination  and  an  area  like  Texas  

188  Dixon,  “Tornado  Risk  Analysis:  Is  Dixie  Alley  an  Extension  of  Tornado  Alley?”;  Forbes,  “What  and  Where  Is  Tornado  Alley?”;  Frates,  “Demystifying  Colloquial  Tornado  Alley:  Deliniation  of  New  Tornado  Alleys  in  the  Central  and  Eastern  United  States”;  Gagan,  Gerard,  and  Gordon,  “A  Historical  and  Statistical  Comparison  of  ‘Tornado  Alley’  to  ‘Dixie  Alley.’”  

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seems  to  be  the  most  tornado  prone.  Select  another,  and  a  state  like  Alabama  suddenly  

comes  into  relief.    

  Uncontested  in  this  boundary  work  of  tornado  risk  classification  is  Oklahoma.  It  sits  

at  the  center  of  most  maps,  and  within  its  borders,  the  town  of  Norman,  a  historical  nexus  

for  atmospheric  research,  and  adjacent  to  it  a  cluster  of  cities  beleaguered  time  and  again  

by  tornadoes:  Moore,  Oklahoma  City,  and  El  Reno.  The  landscape  here  undulates  quietly  

under  the  violence  of  collisions  above,  where  cold  air  off  the  Rockies  collides  with  warm  

moist  air  off  the  Gulf  and  hot,  dry  air  from  Mexico.  These  elements  come  together  in  the  

spring  to  trigger  massive  storms,  called  supercells,  within  which  develop  the  vortex  of  wind  

that  extends  from  cloud  base  to  ground,  the  storm  the  Choctaw  who  first  settled  this  red  

earth  call  “Mahli  Chito,”  tornado189    

  Other  “Alleys”  have  emerged  on  maps  over  the  past  decade  as  populations  expand  

into  larger  urban  centers  and  as  more  people  witness  tornadoes,  capturing  them  in  their  

experiences  and  their  technologies.  The  National  Weather  Service  Storm  Events  Database,  

an  official  accounting  of  storms,  bears  this  out.  It  shows  an  increase  in  the  number  of  

tornadoes  over  the  past  fifty  years,  an  observation  researchers  suggest  derives  not  from  

more  storms  but  an  increase  in  the  number  of  people  who  report  them.190  Dixie  Alley,  in  

the  Southeast,  for  example,  has  arisen  in  the  narrative  of  U.S.  tornado  history  as  a  

competitor  to  Alley  in  the  Plains.  In  number  of  deaths,  nocturnal  occurrence,  population  

vulnerability,  tornado  severity,  and  seasonal  frequency—characteristics  that  

meteorologists  use  to  measure  risk—Dixie  Alley  trumps  Tornado  Alley.  Its  storms  are  

189  “Lesson  of  the  Day.”  190  Verbout  et  al.,  “Evolution  of  the  US  Tornado  Database:  1954-­‐2003”;  National  Weather  Service,  “Storm  Events  Database.”  

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different  from  those  on  the  Plains.191  Whereas  the  most  common  type  of  storm  in  Tornado  

Alley  is  the  classic  low-­‐precipitation  supercell  (think  “Wizard  of  OZ”)  those  in  the  

Southeastern  U.S.  mainly  form  as  high  precipitation  tornadoes,  or  rain  obscured  winds  that  

make  them  difficult  to  see  in  the  hilly  terrain.  And  more  deadly.    

***  

  That  afternoon,  in  Tornado  Alley  Julie  knew  she  was  in  trouble.  She’d  seen  the  

“swirling  green  and  red  colors”  that  represented  the  radar  signature  of  the  storm  on  a  

colleague’s  cell  phone  and  knew  it  would  be  bad.  “I’d  seen  this  form  on  TV  several  times  so  

I  knew  what  it  meant.  It  meant  the  tornado  was  coming  at  us.”  

  As  an  executive  secretary  to  the  safety  administrator,  Julie  had  some  disaster  

training.  She’d  been  told  the  year  before  that  she  needed  to  participate  in  the  scenarios.  Her  

boss,  a  tall,  stout  man  in  his  mid-­‐thirties,  and  director  of  safety  administration  for  facilities  

within  the  local  medical  system,  told  her,  “You  never  know  when  you’ll  be  the  only  one  in  

the  office  when  something  happens.”  She’d  laughed,  “I  couldn’t  even  imagine  that  I’d  need  

all  that  training  but  I  did.  And  thank  God  I  had  it.”  On  the  day  of  the  tornado,  several  safety  

staff  at  the  hospital  had  decided  to  head  home  to  be  with  families.  “Some  went  to  go  get  

their  kids  and  I  didn’t  blame  them,”  she  said.  

  Julie  knew  she  would  be  the  one  to  make  decisions  based  on  that  information.  She  

set  about  organizing  people  in  the  hospital,  preparing  them  for  the  worst.  She  explained  

that  she’d  begun  to  run  out  of  room  in  the  hospital  for  people  and  their  pets  to  shelter.  

Many  were  exposed  between  the  interior  wall  and  the  glass  windows  on  the  exterior.  She  

knew  this  wasn’t  safe  but  she’d  already  filled  many  of  the  smaller  offices  and  patient  rooms   191  Ashley,  “Spatial  Analysis  of  Tornado  Fatalities  in  the  United  States:  1880-­‐2005”;  Ashley,  Krmenec,  and  Schwantes,  “Vulnerability  due  to  Nocturnal  Tornadoes.”  

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with  people.  “[W]hat  could  I  do?  I  was  running  out  of  space.  I  mean,  one  woman  was  in  

labor  next  to  a  pit  bull.  And  there  were  a  lot  of  people  who  didn't  speak  English.  They  were  

frightened  and  had  nowhere  else  to  go  so  they  came  to  the  hospital.  It  was  a  safe  place.”    

  Julie  tried  to  think  of  other  ways  to  configure  the  crowd  so  they’d  survive,  especially  

those  who  were  the  most  vulnerable  so  she  decided  to  put  them  all  in  the  cafeteria  on  the  

first  floor.  It  was  the  largest  open  area  in  the  hospital  and  she  knew  she  could  fit  most  of  

those  who  might  need  additional  help,  including  many  of  the  hospital  staff.  As  an  interior  

room,  the  cafeteria  had  no  windows  and  would  be  insulated  by  the  hallway  and  exterior  

wall.  “In  all  I  guess  we  had  about  350  people  taking  shelter  in  the  cafeteria  and  in  the  small  

rooms  on  the  first  floor  of  the  hospital,”  she  said.  That’s  a  lot  of  souls  to  be  responsible  for  

and  she  felt  that  pressure.  “I  prayed,”  she  said.  She  closed  her  eyes  as  she  said  this.  “Hard.”    

  The  room  buzzed  with  the  restless  chatter  of  people  uncertain  of  what  they  might  

have  to  endure.  They’d  come  to  the  hospital  for  procedures,  treatments,  convalescence,  and  

now  they  might  die  from  an  act  of  nature.  Some  people  were  crying  and  consoling  each  

other.  Others  stayed  quiet,  unsure  of  what  to  do.  

  Julie  tried  to  calm  herself  so  she  could  think.  She  had  an  idea.    

  “I  stood  up  on  a  chair  in  the  middle  of  the  room.    Now,  I'm  not  very  tall  so  I  yelled  to  

everyone  in  the  room,  ‘Look  at  me!  Notice  what  I'm  wearing!  Listen  to  the  sound  my  voice!’  

I  said,  ‘I'm  going  to  tell  you  how  we  can  stay  safe  and  I  need  you  all  to  listen.’”  I  could  see  

her  in  my  mind,  in  her  office  attire,  cupping  her  hands  to  her  mouth  as  she  yelled,  the  room  

growing  quiet.  I  could  also  imagine  those  in  the  room  who  didn’t  know  Julie  or  the  position  

she  represented  at  the  hospital  wondering  who  this  woman  was  that  they  should  put  their  

lives  in  her  hands.    

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  She  likely  had  little  time  to  consider  peoples’  concerns  about  her  qualifications.  

They  couldn’t  know  that  she’d  survived  one  historic  tornado  by  enacting  advice  she  likely  

heard  televised  and  broadcast  with  every  potential  tornado.  They  couldn’t  know  about  her  

boss’s  decision  to  include  her  in  hazard  training.  They  couldn’t  know  she’d  save  them.    

   Julie  gave  directions.  To  be  sure  she’d  have  enough  help  after  the  tornado  struck.  

She  corralled  the  half  dozen  physicians  on  staff  that  day  and  gave  them  an  order  that  

stunned  them.  She  opened  the  doors  to  the  cafeteria’s  large  metal,  walk-­‐in  refrigerator.  

“Get  in,”  she  told  them.  “At  first  they  protested.  They  wanted  to  stay  out  and  help.  But  I  told  

them  that  I  needed  them  for  triage.  That  I  needed  them  to  stay  safe.  And  so  they  finally  let  

me  put  them  in.  I  locked  the  door.”    

  Next,  she  sent  a  security  guard  up  to  check  on  a  woman  who  was  in  labor  and  

couldn’t  be  moved  because  of  her  epidural.  Two  nurses  had  volunteered  to  stay  with  the  

patient  on  the  second  floor  and  Julie  wanted  to  see  that  they  were  okay.  Later  the  nurses  

would  detail  how  the  winds  had  come  up  around  them  as  they  huddled  under  blankets  

with  their  pregnant  patient.  They  had  moved  her  to  the  hallway  to  keep  her  from  the  

exterior  windows  but  couldn’t  find  outlets  to  let  them  keep  the  monitors  plugged  in.  They  

moved  her  back  into  the  room  just  before  the  tornado  hit.    

  Finally,  Julie  directed  all  the  nurses  to  help  her.  “We  put  all  the  moms  with  new  

babies  in  the  center  of  the  cafeteria  and  we  create  a  circle  of  nurses  around  each  one.  Then  

we  took  all  the  people  in  wheelchairs  and  gently  lay  them  out  on  the  floor  and  cover  them  

with  blankets  and  tables.  Everyone  else  formed  a  circle.  We  held  hands.  Some  of  us  cried.”  

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  The  tornado  would  be  at  the  hospital  at  any  moment.  Julie  knew  this  because  her  

boss,  Shane,  the  Director  of  Safety  for  this  and  other  medical  centers  in  the  area,  had  just  

texted  her  two  simple  words:  “Code  Black.”    

  “I  didn't  know  what  that  meant.  I  had  to  turn  over  my  badge  and  look  at  the  color  

code  to  decipher  his  message.    Black  meant  imminent  danger.  I  knew  Shane  was  telling  me  

we  were  about  to  get  hit.  So  I  told  everyone  to  join  arms  with  their  backs  to  the  wall.  There  

was  a  lot  of  crying  and  a  lot  of  praying.”    

 

  There  was  likely  a  lot  of  praying  in  the  Norman  forecast  office,  as  well.    Located  10  

miles  south  of  Moore,  Norman  is  a  hub  for  many  meteorological  activities.  The  University  of  

Oklahoma,  home  of  a  top  ranking  meteorology  program,  sits  near  its  heart,  along  with  the  

National  Weather  Center,  a  250,000  square  foot  building,  which  houses  several  key  

government  weather  prediction  centers  and  research  labs,  such  as  the  National  Severe  

Storms  Lab  and  the  Hazardous  Weather  Testbed.  Clustered  together  on  the  third  floor,  two  

interlinked  centers  sit  adjacent  to  one  another  separated  by  glass.  The  Storm  Prediction  

Center,  a  national  unit  in  the  National  Oceanic  and  Atmospheric  Administration,  is  

responsible  for  monitoring  for  threats  from  tornadoes,  severe  thunderstorms,  wildfires,  

and  winder  weather  and  issuing  watches.  To  its  left,  is  the  local  National  Weather  Service,  

one  of  122  across  the  country  responsible  for  issuing  warnings  and  advisories  for  severe  

weather  in  their  County  Warning  Area.  This  geopolitical  area  consists  of  48  counties  in  

Oklahoma  and  8  in  West  Texas  and  includes  about  2.5  million  people,  who  rely  on  the  office  

for  daily  forecasts  and  hazard  warnings.192  On  May  20,  2013,  thousands  of  individuals  like  

192  Marsh,  “Population  of  NWS  WFOs.”  

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Julie  would  have  found  themselves  inscribed  within  the  red  box-­‐like  shapes  that,  on  

television  and  mobile  phones,  constitute  a  tornado  warning.  

  As  the  storms  blistered  across  their  area,  forecasters  in  Norman  sent  out  alerts.  In  

their  office,  the  air  filled  with  the  buzz  of  voices,  some  emanating  from  the  wall  of  television  

screens  at  the  front  of  the  room,  offering  what  forecasters  call  “situational  awareness”  of  

local  news  coverage.193  Other  voices  came  from  those  on  the  telephone  with  emergency  

managers,  government  officials,  first  responders,  and  concerned  citizens  calling  in  for  more  

specific  information.  HAM  radio  operators  communicated  with  their  storm  spotters  in  the  

field,  the  equipment  itself  “talked”  to  the  forecasters,  warning  them  of  deadlines  for  

forecast  products  or  reciting  the  warning  information  conveyed  over  NOAA  weather  radio.  

But  the  most  haunting  voices  came  from  the  forecasters  talking  to  each  other,  and  at  times,  

the  tornadoes  on  the  television  screen.    

  According  to  field  notes  shared  publicly  by  Jack  Friedman,  an  anthropologist  who  

conducted  observations  in  the  NWS  office  that  day,  the  forecasters  felt  a  sense  of  dread  and  

exhaustion  watching  the  wedge-­‐shaped  mass  on  the  screen.194  Friedman  had  joined  a  panel  

of  meteorologists  representing  different  points  of  view  of  that  day,  from  those  in  the  

National  Weather  Service,  those  who  are  researchers  in  that  community,  those  who  are  

studying  the  warning  system.  All  of  these  people  live  in  these  neighborhoods,  have  taken  

shelter  from  tornadoes  or  lost  parts  of  their  homes  to  storms—roofs,  fences,  trees.  

Collectively,  they’d  designed  this  presentation  to  help  experts  in  the  weather  community  in  

attendance  understand  the  lived  experiences  and  timeline  of  events  that  unfolded  in  May.  

193  NWS  Norman  Forecast  Office  /  May  20,  2013.  194  Correia,  Jr.  et  al.,  “Forecasting  and  Response  to  the  20  May  2013  Oklahoma  City  Area  Tornado.”  

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As  Friedman  read  excerpts  of  what  he  saw  that  day  in  the  forecast  office,  the  audience  

quieted,  the  atmosphere  of  the  room  thickening  with  anticipation  as  he  spoke.  He  began  by  

describing  how  the  forecasters  took  calls  from  the  public  and  storm  spotters,  reporting  the  

first  appearance  of  the  tornado  that  would  hit  Moore  around  2:55  pm.    

  “Someone  said,  ‘This  is  going  to  be  a  big  one,’  or  ‘a  significant  one.’”      

  He  read  slowly,  stoically,  letting  the  images  of  that  day  take  us  to  the  office  with  him.    

  “Several  people  are  on  the  phone  getting  the  reports  coming;  others  have  run  up  and  

are  taking  photos  out  the  window  or  even  pictures  of  the  FO  [forecast  office].  The  

tornadoes  seem  to  be  moving  into  Western  Moore  and  look  to  be  catastrophically  big,  as  

somebody  said,  ‘really  huge  and  really  well  formed.’  There’s  a  much  more  palpable  panicky  

feel  because  this  is  home  for  people.”    

  I’ve  been  in  this  office  before  and  know  some  of  the  meteorologists  well.  They  have  

talked  to  me  about  what  it’s  like  to  be  a  forecaster  in  Norman.  That  day  was  the  third  in  a  

weeklong  streak  of  tornadoes  occurring  nearly  every  day  in  Oklahoma.  They  had  been  

working  overtime,  not  only  issuing  their  regular  forecasts,  which  continue  through  severe  

weather,  but  the  warnings,  as  well.  They  likewise  were  responsible  for  talking  to  a  host  of  

decision  makers  and  members  of  the  public.  And  after  each  tornado,  a  subset  of  the  office  

heads  out  into  the  community  to  conduct  damage  surveys,  walking  slowly  through  the  

debris,  noting  the  characteristics  of  damage,  the  types  and  severity  of  destruction,  which  

they  calculate  in  their  software  in  order  to  come  up  an  Enhanced  Fujita  Scale,  or  EF  Scale,  

rating  for  the  storm.  Often,  they  are  some  of  the  first  on  the  scene,  functioning  as  first  

responders  in  their  encounters  with  emotionally  distraught  victims  who  have  lost  

everything,  including  loved  ones.    

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  The  anthropologist  slowed  a  bit  as  he  described  what  happened  next.  

  “Someone  just  said  out  loud,  ‘Please  weaken,  please  weaken.’  His  leg  is  shaking,  and  

he’s  saying,  ‘Oh  my  god.’    

  “There’s  another  possible  tornado  around  Lawton  that  people  are  trying  to  pay  

attention  to  but  it’s  difficult  when  this  [area]  is  everyone’s  home.    

  “Someone:  ‘It’s  moving  into  Moore  and  southwest  OKC.’”    

  “Someone:  ‘It’s  moving  right  over  my  aunt’s  house.’”  

  “Someone  else:  ‘How  many  times  do  we  have  to  deal  with  this?’”    

  ‘Someone  else:  ‘This  is  going  to  be  a  long  track,  significant  tornado  through  Moore.  

There  are  no  signs  of  occlusion  or  weakening.’”    

  Members  of  the  audience  gasped,  stiffened  as  they  tried  to  stifle  emotions,  though  

some,  like  myself,  quietly  let  the  tears  come.  Those  on  the  panel  were  no  doubt  reliving  the  

fear  they  had  for  their  neighbors,  their  friends  and  families.    

  During  the  Q  and  A,  one  of  the  panelists  noted  he  was  constantly  calling  his  family  to  

make  sure  they  were  in  the  shelter.  That  his  wife  had  picked  up  their  daughter  from  school  

with  enough  time  to  get  back  home.  Another  told  us  how  he  said  goodbye  to  his  apartment  

before  heading  to  a  shelter.  “I’m  sick  of  constantly  saying  goodbye  to  that  place.”  The  

anthropologist,  who  joined  them  on  the  damage  survey,  mentioned  the  sound  of  the  dying  

horses,  which  were  still  being  located  and  put  to  sleep.  “I  can’t  even  describe  that  scene,”  he  

said.  The  other  panelists,  who  had  been  part  of  the  survey,  as  well,  nodded  and  lowered  

their  heads.  I’m  reminded  of  stories  from  the  front  during  the  First  World  War  and  the  

descriptions  of  animals  left  on  the  field  suffering  during  the  night,  unable  to  be  rescued  

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because  of  continual  bombing.  I  think  of  shell  shock.  Post-­‐traumatic  stress.  The  endurance  

of  tragedy  and  its  bodily  toll.    

  There  were  questions  about  the  details  of  recovery,  how  they  notified  the  public  of  

the  threats,  what  they  would  do  differently.  

  “I’m  wondering  if  during  the  damage  survey  you’ve  ever  found  a  body,”  one  woman  

asked.    

  The  question  caused  many  in  the  audience  to  shift  in  their  seats.  People  whispered  

about  the  appropriateness  of  the  question,  the  directness  of  it  in  this  context.  But  to  my  

mind,  it  was  the  perfect  question,  a  symbol  of  the  raw  experience  we  faced  in  this  room.  

  One  of  the  panelists,  a  forecaster  with  the  National  Weather  Service,  looked  down  

for  a  minute.  Then  he  said,  still  looking  down.  “I  never  have,  thankfully.  If  I  ever  did,  that  

would  be  the  end  for  me.  My  retirement.”  He  looked  up  at  the  crowd.  “It’s  not  something  I’d  

recover  from.”  

 

***  

  Julie  already  had  survived  two  tornadoes  classified  by  winds  over  200  miles  per  

hour.  Her  first  occurred  on  May  3,  1999,  when  the  now  infamous  EF-­‐5  that  struck  the  same  

community  of  Moore.  From  that  experience,  she’d  learned  a  great  deal  about  how  to  read  

the  skies,  the  technologies,  and  the  people  around  her.    

  She’d  been  doing  home  improvement  projects  with  her  boyfriend  when  she  heard  

the  sirens  blare.  “I  remember  going  to  the  bathroom,”  she  said,  “which  was  the  smallest  

room  in  the  house  and  it  didn't  have  any  windows.  I  had  my  little  dog  in  my  purse  around  

my  neck.  But  we  were  renovating  the  bathroom  and  so  there  was  no  door.  My  husband  

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wedged  our  white  couch  into  the  doorframe  just  a  few  minutes  before  we  heard  the  

tornado  coming.”  She  continued:    “You  know,  people  talk  about  it  sounding  like  a  train  or  a  

bunch  of  bees  but  me  it  sounded  like  metal  on  metal.  It  hit  the  house  and  I  thought  for  sure  

we  would  die.  It  seemed  like  the  wind  lasted  several  minutes  but  it  was  over  quickly.  And  

you  know  that  white  couch  didn't  budge.  It  stayed  put  and  kept  all  the  debris  from  hitting  

us.”  

  No  doubt  she  drew  on  her  emotions  from  that  day,  her  understanding  of  the  terror  

of  uncertainty—would  they  survive?  And  if  they  did,  what  would  remain?  

  In  recounting  to  Laura  and  me  the  minutes  before  the  tornado  hit,  Julie  paused,  as  

though  the  interview  were  over.  She  looked  at  us,  through  us,  like  weren’t  in  the  room.  We  

knew  she’d  put  the  doctors  in  the  refrigerator,  sent  nurses  upstairs  checking  on  a  patient,  

and  had  others  encircled  new  mothers,  their  babies,  and  people  in  wheelchairs  who  had  

been  laid  down  on  the  ground.    

  Laura  looked  at  me  and  then  cleared  her  throat.  “That  must  have  been  so  terrifying,”  

she  said.    

  “I  can’t  imagine,”  I  added  quickly,  “what  it  would  have  been  like.”    

  Julie  turned  to  the  side  in  her  chair  and  looked  down  to  her  lap.  She  picked  at  

something  on  her  skirt,  then  looked  up  again  at  us.  “You  two  ladies  seem  like  good  

Christian  women,”  she  said  after  the  long  pause.  “So  I’ll  tell  you  what  happened.”  

  I  nodded,  and  I’m  sure  I  blushed.  I  didn’t  identify  as  Christian  and  wondered  if  I  

should  say  something  before  she  continued.  Or  perhaps  she  invoked  a  religious  label  to  

suggest  to  us  that  she  trusted  us  and  how  we  would  treat  what  she  was  about  to  say.  Either  

way,  I  felt  the  room  suddenly  close  in  a  little  as  she  began.      

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  “We  were  all  in  the  cafeteria  waiting  for  the  tornado  to  hit  when  a  huge  wind  rushed  

to  the  building  and  blew  the  doors  inward.  It  caught  one  of  the  security  guards  and  she  

rolled  into  the  room  through  the  doors.    I’d  sent  her  to  do  a  quick  check  of  the  hallways  to  

make  sure  we  had  everybody.    

  “Then,  there  was  this  loud  sound,”  she  said,  “a  bunch  of  things  hitting  the  building,  

some  loud  bangs.”  She  looked  just  above  us  as  she  spoke.  “And  then  it  was  in  the  room  with  

us.”  

  She  said  the  tornado  appeared  as  a  grey  mass  with  things  swirling  in  it,  the  sound  

drowning  out  everything  else.  “After  a  few  seconds  I  broke  rank,”  she  said,  “turned  around  

and  faced  the  wind.  I  stretched  out  my  right-­‐hand  at  the  tornado  and  started  yelling,  ‘In  the  

name  of  Jesus  Christ  I  command  you  to  leave!  In  the  name  of  Jesus  Christ  I  command  you  

leave!’”    

  The  other  nurses  started  cheering.  “They  yelled,  ‘It's  working  Julie!  It's  working  

Julie!’  And  you  know  that  tornado  it  lifted  up  and  went  over  the  hospital.”  

  “You  know  later  the  engineers  explained  it  to  me,”  she  said.  She  leaned  forward  

across  the  table  a  bit,  as  though  she  were  finally  sharing  something  confidential.    

  “They  said  the  tornado  had  gathered  all  these  cars  in  the  parking  and  pushed  up  

against  the  wall  the  cafeteria.  We  were  right  behind  this  cafeteria  doors.  All  those  cars—

some  three  hundred  of  them—were  piled  into  the  doorway  of  the  hospital  and  acted  like  a  

leaver  in  diverting  the  tornado  winds  up  and  over  hospital.  I  mean,  I  know  that  this  is  the  

science  behind  why  we're  alive.  But  we  all  know  that  God  saved  us,  that  he  listened  when  

we  prayed.”    

***  

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  Not  all  feelings  of  responsibility  are  easily  shared,  however,  especially  when  people  

die.  “I  have  the  most  deaths  on  my  watch,”  one  forecaster  said  aloud  to  the  room.  He  kept  

his  back  to  me  as  he  continued  working  on  the  computer  in  front  of  him.    

  “It’s  true,”  another  said,  glancing  my  way.  “For  whatever  reason,  when  Tom  works,  

that’s  when  people  die.”  They  both  smiled  weakly  and  went  back  to  their  work.    

  “It’s  a  curse,”  Tom  mumbled.  His  office  had  just  been  notified  that  someone  had  

drown  during  a  flood  of  the  local  river  when  a  few  young  adults  had  thought  it  a  good  idea  

to  try  and  tube  the  raging  river  as  it  rushed  through  the  canyon.  Tom  had  issued  the  flash  

flood  warning  that  morning  and  by  afternoon,  officials  had  found  the  man’s  body.    

  I’m  reminded  of  my  first  National  Weather  Association  meeting  in  Birmingham,  

Alabama,  a  society  comprised  mostly  of  operational  forecasters.  The  state  had  just  been  

ravaged  by  some  350  tornadoes  weeks  before,  the  destruction  still  evident  around  us  in  

neighborhoods  close  by.  During  one  of  the  session  breaks,  a  forecaster  at  a  local  National  

Weather  Service  stood  in  front  of  his  colleagues  with  a  microphone.  “Look,”  he  said,  “I  know  

many  of  you  want  to  know  how  I’m  doing—how  my  staff  is  doing.    And  I  appreciate  your  

concern.”  He  took  a  deep  breath,  letting  it  out  slowly  to  stifle  the  crack  in  his  voice.  “But  I  

just  can’t.  I  can’t  talk  about  what  happened  yet.  I  hope  you’ll  respect  that.”  He  handed  the  

mike  to  a  person  standing  nearby  and  wiped  at  his  eyes.    

  Later  that  week,  at  the  awards  ceremony,  a  member  of  the  community  stood  and  

gave  an  invocation  before  the  program  began.  

    “Dear  Lord,”  he  began.  “Guide  us  in  our  predictions  that  our  judgments  may  be  clear  

and  our  forecasts  true.”  

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  All  heads  bowed,  arms  folded  or  placed  carefully  on  the  table.  The  prayer  continued,  

asking  God  to  bless  the  community  in  their  daily  jobs  as  public  servants.  It  asked  that  He  

watch  over  those  who  had  lost  homes  and  loved  ones  in  the  recent  storms  and  over  the  

forecasters  still  grappling  with  their  own  emotions.  I  could  hear  people  sniffling  as  he  

spoke.    

  Later,  forecasters  in  Alabama  were  able  to  talk  about  what  had  happened.  They  put  

together  a  series  of  videos  to  explain  their  experiences  with  the  tornadoes  that  year.195  The  

Meteorologist  in  Charge,  Chris  Darden,  opened  the  video  with  a  mix  of  explanation  and  

description  of  those  days.  “Let’s  be  clear,”  he  said,  “we  all  are  professionals  and  we  all  have  

a  job  to  do.”  However,  as  the  storms  rolled  through  one  community  after  another,  he  

admitted,  “We  were  all  visibly  shaken.”  

  In  one  particular  community,  forecasters  saw  on  their  radar  screens  the  shape  of  

something  that  represented  a  “debris  ball”  or  the  detritus  of  the  storm  being  picked  up  by  

their  instruments.  “That’s  when  we  knew  it  was  bad,”  Chris  forecaster  said.  Another  noted  

that  whatever  had  been  there  before  was  gone.    

  The  video  overlaid  scenes  of  destruction  with  the  colorful  splotches  on  radar.    

Images  shifted  from  their  work  to  the  damage  on  the  ground,  from  them  at  their  desks  to  

people  standing  among  ruins.  “Damage  reports  were  slow  to  roll  in  from  a  few  locations,”  

Chris  said  in  the  voice  over,  “and  we  knew  why.  It  wasn’t  that  this  wasn’t  a  damaging  

tornado,  it  was  because  there  was  no  one  left  to  report  the  damage.    In  our  gut  we  knew  

there  were  mass  injuries  and  very  likely  a  large  number  of  fatalities.”  

195  National  Weather  Service,  NWS  Huntsville:  A  Look  Back  on  the  April  27th  Outbreak-­‐-­‐Part  1.  

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  The  video  cut  to  an  image  of  the  tornado  from  that  day,  a  smudgy  gray  shape  on  the  

horizon,  taking  up  much  of  the  television  screen.  “We  can  only  pray  that  our  warnings,  

updates,  and  diligent  work  could  prevent  this  from  being  a  complete  catastrophe,”  Chris  

said.  He  continued  and  explained  that  in  the  days  following  the  tornadoes,  forecasters  

spent  “many  16-­‐hour  days  helping  first  responders”  and  “just  being  there  for  our  

communities.”    

  I  imagine  the  aftermath,  pine  trees  broken  in  half,  houses  gutted,  the  smell  of  lumber  

and  the  pungent  remnant  of  gasoline  from  overturned  cars  thick  in  the  air.  People  would  

have  been  milling  about  the  litter  strewn  in  their  yards  or  where  there  homes  once  stood,  

wandering  through  wreckage  to  find  that  one  photograph  or  toy,  anything  that  might  be  

salvaged.  Forecasters  are  often  first  to  arrive  in  some  of  the  more  rural  parts  of  the  county,  

the  first  to  speak  to  those  who  have  already  begun  to  make  sense  of  what  had  happened.  

  “The  scenes  of  destruction  out  there  were  sobering,”  Chris  said,  “even  for  a  seasoned  

meteorologist.  As  someone  who  has  been  on  nearly  50  storm  surveys,  seeing  entire  

neighborhoods  brought  to  the  ground  was  horrendous….gut  wrenching…almost  too  much  

to  bear.”  Stories  of  survival,  he  offered  give  us  “a  little  piece  of  joy  in  a  large  sea  of  despair.”  

  The  phrase  “on  my  watch”  connotes  personal  obligation  and  accountability  for  

whatever  happened  while  a  person  was  in  charge.  It  implies  an  all-­‐seeing  eye,  a  capacity  to  

observe  the  infrastructural  and  the  intimate  and  then  to  control  them.  Prevent.  Protect.  

Intervene.  This  is  perhaps  where  the  phrase  comes  apart.  The  first  person  claims  

responsibility  for  more  than  perhaps  ought  to  be  overseen.  A  shared  sense  of  duty—our  

watch—a  collective  effort  to  distribute  the  efforts  of  watchfulness  seems  more  reasonable.  

More  ethical.  Putting  on  one  person  the  onus  to  account  for  things  that  ought  to  be  viewed  

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collectively  elides  opportunity  for  change  as  it  masks  moments  of  success  and  failure  in  the  

larger  apparatus  of  disasters.    

  A  makeshift  memorial  had  arisen  amid  the  remains  of  a  school  in  Moore  where  

seven  children  died,  a  response  to  the  community’s  grief.  Seven  small  chairs  had  been  

arranged  in  a  semi-­‐circle  behind  the  chain  link  fence  now  surrounding  the  empty  

foundation.  Behind  them,  wooden  crosses  set  in  the  ground  marked  the  children’s  deaths  

and  on  the  front  of  each,  a  placard  with  the  child’s  name.  Within  the  links  of  the  fence  and  

against  its  base,  people  from  across  the  country  had  placed  teddy  bears,  t-­‐shirts,  flowers,  

flags,  dolls,  and  other  personal  items.  We  all  mourned,  felt  these  deaths  senseless,  

unbearable,  a  tragedy  without  blame.  Teachers  had  instructed  children  to  crouch  in  the  

windowless  center  of  the  school,  in  bathrooms  and  closets.  They  did  all  they  could.  The  

warnings  were  issued,  sirens  sounded,  actions  taken,  and  under  the  darkened  skies  that  

day,  these  seven  had  died.    

***  

 

  The  twister  began  as  a  wisp  of  gray  wind  swirling  over  a  green  line  of  fields  near  a  

freeway  in  Oklahoma.  Like  a  pencil  on  a  map,  the  tornado  quickly  scrawled  its  mark  across  

the  landscape  for  nearly  twenty  miles,  gaining  in  speed  to  become  at  its  widest,  1.3  miles  in  

diameter.    Over  the  course  of  forty-­‐seven  minutes,  the  tornado  moved  through  

communities,  growing  and  ebbing  in  strength,  killing  two  dozen  people  and  injuring  nearly  

400  more.    

  News  media  outlets  began  broadcasting  live  coverage  of  the  wedge-­‐shaped  wind  as  

it  scoured  the  ground  for  miles.  It  struck  a  horse  training  area,  Celestial  Acres,  where  it  

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tossed  horses  into  power  lines  and  impaled  them  with  debris.  Minutes  later,  the  tornado  

would  destroy  the  first  of  two  elementary  schools,  then  the  second,  where  seven  children  

would  die  as  they  lay  trapped  in  debris,  drowned  by  heavy  rains  after  the  storm  passed.    It  

leveled  house  after  house  in  its  path,  some  structures  torn  apart,  their  foundations  wiped  

clean.196  Others  were  unrecognizable  as  buildings,  the  shredded  wood  and  wire  protruding  

from  debris  like  bones.    

  Around  3:15  pm,  the  tornado  headed  for  the  hospital,  a  glass  and  cement  building  

filled  with  over  350  people,  many  of  whom  were  simply  fleeing  the  storm.  In  a  news  article  

written  days  after  the  disaster,  one  hospital  administrator  noted  that  their  facilities  had  

experienced  an  increase  in  the  number  of  people  coming  to  them  for  protection.  During  the  

storm,  they  had  had  “a  basement  full  of  folks  who  had  nowhere  else  to  go.”197  People  began  

arriving  hours  before  the  tornado,  bringing  whatever  they  could  carry.  At  the  forecast  

office,  meteorologists  weighed  the  tornado  on  scales  of  technologies  built  of  their  collective  

experiences  and  the  expertise  shaped  by  their  technologies.  

  The  Weather  Channel  played  reruns  showing  wrecked  buildings  leveled  across  

several  small  towns  and  cities.  Anchors  on  air  yelled  in  horror  as  the  rotating  winds  struck  

the  two  elementary  schools,  local  businesses,  the  town’s  only  movie  theater.  Aerial  footage  

revealed  rows  of  homes  shredded  and  mangled,  cars  upended  in  yards,  trees  stripped  and  

flattened.  From  high  above,  the  tornado’s  path  seemed  to  clear-­‐cut  through  thousands  of  

lives.  Somewhere  in  that  wreckage,  Julie  was  making  her  way  out  of  the  debris.  Forecasters  

had  begun  their  reckonings.  

196  Huntsville-­‐Madison  County  Emergency  Management  Agency,  “Alabama  Tornado  Outbreak.”  197  Carlson,  “Seeking  Shelter:  As  Tornado  Bore  Down,  Residents  Flocked  to  Hospital.”  

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  Recovery.  I  find  this  word  unsatisfying  in  the  context  of  disasters.  The  notion  that  

we  can  return  to  what  was  before,  to  get  back,  as  the  etymology  of  the  word  suggests,  

doesn't  bear  out  in  practice.  People  instead  function  at  a  “new  normal”  that  can  be  more  

difficult,  less  stable,  and  materially  different  than  the  previous  one.  Yet,  some  hazards  and  

disaster  literature  centers  on  recovery  as  one  of  the  main  aspects  of  what  they  call  a  

disaster  cycle.  Others  rebrand  recovery  as  resilience,  what  Kathleen  Tierney  defines  as  “the  

inherent  and  preexisting  qualities  and  attributes  that  enable  at-­‐risk  entities  to  absorb  

stresses  caused  by  eternal  shocks”  combined  with  “adaptive  or  post  event  activities  and  

processes  that  enhance  coping  capacity.”198  Moving  toward  a  new  normal  is  indeed  a  goal  

of  communities,  like  Moore,  that  have  been  devastated  time  and  again.  But  in  the  narrative  

of  recovery,  and  the  attending  subthemes  of  resilience,  vulnerability,  and  justice,  just  who  

recovers,  who  decides,  and  at  what  cost  are  rarely  addressed  within  the  discussion.  And  in  

this  literature,  emphasis  on  the  emotional  recuperation  of  the  community  extends  to  first  

responders,  those  like  medical  and  law  officials,  fire  fighters  and  police  officers,  as  well  as  

emergency  managers.  But  others  fall  outside  the  visible  scope  of  community  recovery.    

  One  person  in  the  audience  listening  to  the  panel  raised  this  issue:  “Are  there  

resources  available  to  you  all  in  terms  of  PTSD,  or  do  you  know  if  research  has  been  

conducted  on  the  effects  of  extreme  weather  on  forecasters?”      

  It  seemed  an  obvious  question  at  the  moment,  but  no  one  in  the  room  could  think  of  

any  studies.  One  of  the  panelists  responded  that  the  agency  had  started  making  counseling  

available  to  National  Weather  Service  staff  after  2011  when  a  series  of  deadly  tornadoes  

198  Tierney,  The  Social  Roots  of  Risk:  Producing  Disasters,  Promoting  Resilience,  69.  

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killed  hundreds  in  the  Southeast.  I’d  spoken  with  forecasters  in  forecast  offices  in  Alabama.  

Five  years  later,  one  person  broke  down  in  front  of  a  crowd  as  he  relived  the  days  after  the  

EF-­‐4  tornado  killed  100  people  in  his  area  and  injured  nearly  another  hundred.  He’d  been  

out  with  the  damage  survey  team  after  working  virtually  non-­‐stop  for  the  past  24  hours  in  

the  forecast  office  when  they  encountered  a  teenager  sitting  on  the  steps  of  a  house  that  

had  been  swept  away  by  the  tornado.  Nothing  remained  of  his  home.  After  talking  to  the  

boy  for  a  bit,  offering  him  some  of  their  lunch,  the  boy  walked  them  across  his  empty  

foundation,  pointing  out  where  he’d  found  his  mother’s  body,  his  father’s.  “My  wife  says  it’s  

good  for  me  to  talk  about  what  happened,”  he  said  as  he  wiped  away  tears.  “It  is  slowly  

getting  better.”    

 

  National  Weather  Service  assessments  of  weather  disasters  written  after  the  fact  

offer  a  vivid  description  of  the  meteorological  conditions  that  led  to  the  storm,  what  

warnings  were  issued  and  when,  what  technologies  failed  and  how,  and  what  behaviors  

were  taken—by  forecasters,  decision  makers,  and  members  of  the  public.199  How  could  

Laura  and  I  distill  or  even  begin  to  include  as  a  “best  practice”  the  way  Julie  handled  the  

unique  circumstances  of  her  responsibility  to  the  hospital  and  all  who  entered  that  day?  

What  problem  might  we  identify  in  how  forecasters  negotiated  their  fear  for  their  

communities  against  the  trauma  of  accountability?  

  If  I  could  have  conducted  my  part  of  that  assessment  differently,  I  might  have  

encouraged  those  we  interviewed  to  tell  us  about  the  things  that  trouble  them  as  they  

contemplate  the  vagaries  of  weather.  What  do  they  wish  others  knew  about  their  lives  and  

199  National  Weather  Service,  “The  Historic  Tornadoes  of  April  2011.”  

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the  way  they  feel  about  that  which  they’ve  survived?  The  questions  would  center  less  on  

the  collection  of  systems  of  risk  in  order  to  get  to  the  personal  and  more  on  individuals  and  

their  relationship  to  one  another  such  that  we  might  see  the  infrastructures  from  new  

places.  I  would  begin  my  intervention  here,  in  the  first  person,  holding  up  mirrors  to  reflect  

the  varieties  of  selves  that  come  into  view  in  moments  of  crisis.  I  offer  myself  as  this  

instrument  and  these  words  as  to  refract  all  that  might  be  seen.  

   

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Article  3:  Weather  Ready  Nation  or  Ready  Weather  Agency?  Developing  an  Ethic  of  Resilience  in  the  National  Weather  

Service  

   

Prologue    

  Over  the  last  few  years,  at  meteorological  society  conferences  and  in  informal  

conversations,  there  has  been  a  lot  of  discussion  about  the  future  of  the  National  Weather  

Service.  Some  of  these  concerns  arise  out  of  continual  discussion  by  some  Congressional  

leaders  about  the  value  of  the  agency  in  an  era  where  private  sector  meteorologists  might  

better  (and  more  cheaply  for  the  government,  they  argue)  create  weather  forecasts  and  

warnings.  In  2005,  for  example,  Senator  Rick  Santorum  proposed  the  National  Weather  

Service  Duties  Act  that  many  felt  would  eliminate  the  ability  of  forecasters  to  dissemination  

their  forecasts  to  different  publics.  He  proposed  to  limit  the  function  of  the  NWS  to  

warnings  and  alerts  only  to  prohibit  government  competition  with  private  industry  200.    In  

2011  and  2014,  articles  in  national  newspapers  appeared,  reviving  questions  about  the  

need  for  a  national  Weather  Service,  given  that  private  entities  can  offer  similar  services201.  

And  as  recently  as  2015,  U.  S.  Congressman  Representative  Jim  Bridenstine  from  Oklahoma  

included  in  his  proposed  legislation,  The  National  Space  Renaissance  Act,  that  forecasters  

in  the  National  Weather  Service  be  prohibited  from  doing  what  private  meteorologists  

might  do,  in  his  opinion,  better:  "Before  commencing  the  development  of  any  [forecasting]  

program,  the  (NOAA)  Administrator  shall  certify  to  Congress  that  no  commercial  capability  

200  Sen.  Rick  Santorum  [R-­‐PA],  National  Weather  Service  Duties  Act  of  2005.  201  Gelman,  “What  Do  Rick  Santorum  and  Andrew  Cuomo  Have  in  Common?”;  Murray  and  Bier,  “Do  We  Really  Need  a  National  Weather  Service?”  

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or  service,  with  or  without  reasonable  modifications,  can  meet  the  requirements  for  which  

such  program  is  being  developed."  He  put  it  more  directly  in  a  statement  to  the  Committee  

on  Science,  Space,  &  Technology  on  June  8,  2016.  National  Weather  Service  forecasters,  he  

wrote,  should  only  provide  “foundational  datasets  that  others  utilize  to  produce  life-­‐saving  

forecasts,  rather  than  duplicating  efforts  and  technologies  that  are  employed  or  could  be  

employed  by  the  private  sector.”202  Others  argue  in  defense  of  keeping  warnings  in  an  

agency  that  can  speak  with  “one  definitive  voice”  about  threats  to  the  public,  and  do  so  with  

more  experience,  local  expertise,  and  “scientific  rigor”  than  many  employed  in  the  private  

sector.203  

  This  parsing  of  duties  between  public  forecasts  offered  at  no  charge  by  the  National  

Weather  Service  and  the  private  sector’s  products  created  for  pay  to  customer  

specifications  is  an  old  debate  in  many  ways.204  Budget  expenditures  for  staffing  are  

“bloated”  some  claim,  noting  that  many  areas  of  the  country  do  not  need  forecasters  getting  

overtime  pay  for  overnight  shifts  worked  during  fair  weather.  And  possibilities  for  cheaper  

technologies—super  computers,  satellites,  automated  observation  systems—may  means  

the  public  may  no  long  need  to  fund  the  government  to  provide  this  underlying  data.205    

Such  pressure  no  doubt  creates  an  imperative  for  the  National  Weather  Service  to  engage  

in  a  political  economy  of  forecasting  that  shapes  the  agency’s  efforts  to  prove  the  value  of  

their  products  and  services.    

202  Bridenstine,  “Private  Sector  Weather  Forecasting:  Assessing  Products  and  Technologies.”  203  Mass,  “Do  We  Need  Local  National  Weather  Service  Offices  If  We  Have  Weather  Apps,  Accuweather,  and  the  Weather  Channel.”  204  National  Research  Council,  “Fair  Weather:  Effective  Partnerships  in  Weather  and  Climate  Services.”  205  Rosenfeld,  “Do  We  Need  the  National  Weather  Service?”  

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  In  response,  Congress  commissioned  two  National  Academies  of  Science  reports  to  

examine  the  staffing  structure  and  possible  inefficiencies  that  could  account  for  or  

substantiate  such  accusations.  The  first,  Weather  Services  for  a  Nation:  Becoming  Second  to  

None,  highlighted  three  recommendations:  technological  development  and  improvements  

in  data  acquisition  and  Numerical  Weather  Prediction;  evolving  staff  structures  to  “utilize  

core  capabilities  and  optimally  serve  the  public,”  including  evaluating  the  number,  type,  

and  arrangement  of  staff  at  local  forecast  offices;  and  identify  “secondary  value-­‐chain”  

services  that  might  augment  their  own  infrastructures.206  Through  these  efforts,  the  agency  

might  “evolve”  its  services  to  better  meet  the  needs  of  society  and  to  do  so  more  efficiently  

and  cheaply.      

  The  second  report,  Forecast  for  the  Future:  Assuring  the  Capacity  of  the  National  

Weather  Service,  elaborated  findings  about  NWS  operations  and  possible  “frameworks  for  

the  future.”  It  specifically  highlighted  the  latest  agency  strategic  plan,  launched  in  2011,  

called  “Weather  Ready  Nation,”  noting  that  this  document  offered  the  agency  an  

opportunity  to  re-­‐examine  its  current  practices  to  “better  align  its  resources  and  

operations”  to  meet  the  needs  of  this  “new  paradigm.”  In  particular,  building  relationships  

with  stakeholders  and  core  partners  emerged  as  a  central  recommendation,  one  that  the  

report  noted  is  “a  new  approach  for  the  NWS  that  embraces  collaboration  and  seeks  new  

ways  to  create  value  beyond  traditional  forecasting  activities.”207  Expanding  their  

collaborative  network  would  move  the  NWS  beyond  products  that  are  similar  to  those   206  Committee  on  the  Assessment  of  the  National  Weather  Service’s  and  Committee  on  the  Assessment  of  the  National  Weather  Service’s,  “Weather  Services  for  the  Nation:  Becoming  Second  to  None,”  3–6.  207  National  Academy  of  Public  Administration,  “Forecast  for  the  Future:  Assuring  the  Capacity  of  the  National  Weather  Service,”  Congressional  Report  (Washington,  D.C.:  National  Academy  of  Public  Administration,  May  2013),  12.  

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created  within  private  industry,  thus  creating  a  value  that  would  be  unique  to  the  NWS  and  

perhaps  more  impervious  to  attack.  To  move  forward,  the  report  suggested  “the  NWS  

conduct  an  NWS-­‐wide  analysis”  of  its  infrastructures,  workforce,  training  plans,  

technological  systems,  and  communications  strategies.208  In  2014,  the  National  Weather  

Service  hired  consulting  firm  McKinsey  &  Company  to  follow  these  recommendations  with  

results  now  available  at  society  meetings  and  in  private  agency  discussions  with  the  

agency’s  union,  the  National  Weather  Service  Employees  Organization.  

  It  is  in  this  context  that  the  following  article  emerges.  As  forward  looking  

documents,  the  “Weather  Ready  Nation”  strategic  plan  and  the  “Weather  Ready  Nation  

Roadmap”  together  instantiate  concerns  over  both  the  value  of  the  National  Weather  

Service  and  its  products,  as  well  as  efforts  to  make  the  agency  more  relevant  through  

relationships  with  government  partners  in  the  public  safety  sector.  These  relationships  get  

codified  in  the  key  Roadmap  initiative,  Impact  Based  Decision  Support  Services,  or  IDSS,  or  

those  policies  that  determine  which  groups  count  as  partners  and  the  activities  and  

practices  forecasters  develop  to  build  relationships.  In  many  ways,  a  Weather  Ready  Nation  

is  also  a  ready  weather  agency,  imbricating  society  and  forecasters  in  a  mutual  effort  to  

survive  their  respective  threats—one  an  external  resilience  of  communities  against  weather  

dangers  and  another  an  internal  resilience  of  the  agency  against  societal  irrelevance  amid  

economic  pressures.  .    

  In  2014,  a  forecaster  at  an  office  where  I  had  been  conducting  observations  asked  if  

I’d  be  willing  to  serve  as  a  subject  matter  expert  in  a  series  of  webinars  held  within  the  

NWS  that  would  give  local  offices  a  chance  to  highlight  IDSS  activities  they  had  created.  

208  Ibid.,  13–14.  

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These  webinars,  he  said,  would  last  two  years.  “They’ll  give  Headquarters  a  chance  to  see  

what  offices  are  doing  for  IDSS  and  what  they  should  be  doing.”  I  was  relatively  new  to  the  

concept  of  IDSS  but  thought  that  participating  in  these  webinars  would  give  me  a  chance  to  

learn  more  about  the  initiative  and,  perhaps,  to  help  shape  its  direction  and  the  kinds  of  

people  who  might  be  invited  to  the  broader  discussion.  After  just  a  few  webinars,  it  became  

clear  that  IDSS  currently  functioned  as  a  flexible  and  grassroots  movement  in  the  NWS.  

Although  administrators  stressed  the  broader  framework  of  IDSS  as  one  that  would  build  

relationships  with  partners,  they  weren’t  sure  yet  exactly  what  IDSS  would  or  should  look  

like.  They  were  taking  their  cue  from  local  offices  and  their  example  activities.  The  IDSS  

conference  portal,  which  is  password  protected  for  NWS  and  facilitators  only,209  notes  that  

“The  goal  of  the  IDSS  Webinars  is  to  develop  a  place  for  our  employees  to  share  Impact-­‐

based  DSS  (IDSS)  examples  and  drive  innovation,  training,  collaboration,  consistency,  and  

coordination  across  all  of  the  NWS.”    Relevant  to  my  interests  in  helping  facilitate  these  

sessions,  we  were  asked  to  “highlight  all  types  of  IDSS”  and  “spotlight  IDSS  provided  to  

emerging  unique  partners.”    

  But  it  is  more  than  this.  For  individual  forecasters,  the  collective  Weather  Ready  

Nation  paradigm  offers  an  open  question  about  who  forecasters  are  and  what  they  are  

for.210  I  examine  the  entanglement  of  Weather  Ready  Nation,  IDSS,  and  resilience  to  make  

this  opportunity  more  clear.  In  the  context  of  the  other  articles  in  this  dissertation,  my  hope  

is  that  this  one  demonstrates  a  range  of  images  available  to  the  forecaster  that  need  not  be  

209  While  the  site  is  protected,  all  video  recordings  of  IDSS  webinars  are  available  on  YouTube  ,  though  they  are  difficult  to  find  with  a  blind  search.  Each  webinar  has  a  unique  url  and  there  is  no  general  category  or  channel  to  which  they  all  belong.  210  Downey,  “What  Is  Engineering  Studies  For?  Dominant  Practices  and  Scalable  Scholarship,”  2009.  

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mutually  exclusive  to  images  forecasters  as  accuracy  experts  but  co-­‐existing  with  care  in  an  

ethic  of  empathetic  accuracy.  My  contribution  to  the  forecasting  community,  then,  is  a  new  

way  to  see  themselves  as  capable  of  retaining  scientific  authority  and  expertise  as  

predictors  but  doing  so  in  the  service  of  their  concern  over  the  lives  and  wellbeing  of  their  

communities.  That  is,  empathetic  accuracy  has  the  potential  to  turn  the  science  of  

forecasting  from  a  singular  enterprise  to  a  plural  one,  such  that  what  it  means  to  be  a  

forecaster  may  include  the  narrower  emphasis  on  accuracy  and  elevate  impact-­‐oriented  

communication  as  part  of  their  science.  While  it  is  true  that  communication  and  

partnership  building  has  been  part  of  their  practice  across  many  contexts,  I  suggest  these  

efforts  have  been  largely  marginalized.  Until  recently,  forecast  offices,  for  example,  had  a  

designated  desk  where  a  forecaster  would  answer  phone  calls  from  the  public—a  “public  

desk,”  as  some  call  it.  Others  throughout  the  office  answer  the  phone  as  needed,  too,  yet  the  

designation  of  a  singular  public  desk  compared  to  several  forecasting  desks,  suggests  

communication  is  less  a  part  of  the  job  and  the  science.  And  there  has  been  just  one  staff  

member  designated  to  building  those  relationships  with  different  partners  in  the  public  

safety  community,  the  Warning  Coordination  Meteorologist.  Further,  the  pull  toward  

accuracy  as  a  dominant  ethic  is  strong  in  the  NWS  and  could  mean  that  IDSS  becomes  yet  

another  system  of  metrics  that  offer  quantified  and  economic  justification  for  a  profession  

that  ought  to  traffic  in  the  care  of  people.  

  This  article  has  been  accepted  to  an  edited  collection  assembled  by  Sulfikar  Amir  

who  is  an  associate  professor  of  sociology  at  Nanyang  Technnological  University  in  

Singapore.  I  joined  several  colleagues  from  across  the  international  Disaster  STS  

community  at  a  2-­‐day  workshop  in  June  2016  to  develop  the  collection,  titled  “The  

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Sociotechnical  Constitution  of  Resilience:  Structures,  Practices,  and  Epistemologies.”  In  the  

abstract  for  the  workshop,  Sulfikar  wrote  about  what  he  envisioned  for  us  as  a  group:  

“…while  there  are  multiple  perspectives  linked  to  resilience,  there  is  still  a  need  for  

understanding  the  structures,  practices  and  epistemologies  related  to  resilience  in  

sociotechnical  systems  as  a  unified  concept.”  In  short,  he  explained,  “We  need  to  build  a  

multidisciplinary  STS  critique  of  resilience.”    

  Through  a  number  of  case  studies  and  theoretical  discussions,  we  focused  on  a  

critical  examination  of  the  term  resilience  and  the  ways  it  is  imbricated  in  the  

sociotechnical  through  what  our  group  identified  as  sociomaterial  structures,  informational  

relations,  and  anticipatory  practices.    The  book  will  focus  on  different  kinds  of  disasters,  

and  ask  questions  such  as,  What  makes  society  resilient?  And,  what  have  we  learned  from  

large-­‐scale  disasters  about  the  role  of  knowledge,  expertise,  and  community  in  resilience  

and  how  to  improve  them?  Other  STS  scholars  in  the  collective  include  Scott  Knowles,  Anto  

Mohsin,  Ashley  Carse,  Katrina  Petersen,  Steven  Healy,  Megan  Finn,  and  several  scholars  

writing  about  Fukishima:  Ryuma  Shineha,  Mikihito  Tanaka,  Kurniawan  Adi  Sputro,  

Hyungsub  Choi,  Khota  Juraku,  among  others.  Another  aim,  then,  is  to  move  beyond  Western  

discussions  of  disasters  to  look  at  how  concepts  like  resilience  and  vulnerability  travel  and  

transform  in  non-­‐Western  contexts,  such  as  in  East  and  Southeast  Asia.  

  Tentatively  titled  Bouncing  Back:  The  Sociotechnical  Constitution  of  Resilience,  our  

collection  will  be  submitted  for  review  to  one  of  three  presses:  MIT  Press,  University  of  

Pennsylvania  Press,  or  Palgrave  McMillian.  I  submitted  an  early  draft  of  this  article  and  

have  revised  it  based  on  that  feedback,  in  addition  to  what  I’ve  received  from  my  

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committee.  I’ll  submit  a  final  draft  of  the  article  to  Sulfikar  in  January..  The  target  word  

count  is  8,000-­‐10,000  words  and  we’re  aiming  for  a  publication  date  sometime  in  2018.  

 

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Weather  Ready  Nation  or  Ready  Weather  Agency?    Developing  an  Ethic  of  Resilience  in  the  National  Weather  Service  

 [Word  Count:  8,281]         Communities,  organizations,  and  government  agencies  have  increasingly  mobilized  

the  concept  of  resilience  in  disaster  discourse  to  develop  strategies  that  might  help  people  

to  return  to  a  state  of  normalcy  after  catastrophes  or  disruptions,  or  adapt  to  ongoing  

changes  in  their  environments.  Yet,  as  this  collection  of  articles  demonstrates,  resilience  is  

a  polysemous  concept  that  eludes  straightforward  definitions  in  its  deployment.  Critiques  

of  resilience,  as  the  introduction  argues,  require  transdisciplinary  approaches  to  

understanding  the  structures,  practices,  and  epistemologies  that  emerge  in  its  wake.    An  

important  aspect  of  resilience  that  is  less  often  addressed  in  disaster  discourse  is  its  

normative  aims,  or  as  Healy  and  Mesman  (2014)  note,  “the  resilience  of  what,  for  whom,  

and  at  what  cost  or  tradeoff…?”211  Such  questions  help  us  understand  the  motives  and  

consequences  of  efforts  that  might  otherwise  be  deemed  innocuous  or  remain  hidden.  They  

may  also  help  reveal  moments  of  possible  intervention.  In  this  article,  I  use  these  questions  

to  examine  normative  dimensions  of  resilience  as  they  emerge  in  the  latest  “roadmap”  for  

the  United  States  National  Weather  Service  titled  “Weather  Ready  Nation.”212  

  Resilience  performs  in  this  document  through  a  collection  of  activities  and  concepts,  

practices  and  policies  instantiated  in  a  new  initiative  called  Impact  Based  Decision  Support  

Services,  or  IDSS.    Together  these  function  as  a  sociotechnical  infrastructure  and  guiding  

framework  for  forecasters’  engagement  with  their  various  publics.  A  reading  of  resilience  

through  a  normative  lens  offers  insight  into  multiple  valences  of  resilience,  which  I  show  

211  Healy  and  Mesman,  “Resilience:  Contingency,  Complexity,  and  Practice,”  155–56.  212  National  Weather  Service,  “Weather  Ready  Nation  Roadmap.”  

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inflects  two  categories  of  threats.    One  valence  points  to  the  external  threat  of  “extreme  

weather  events,”  which  destroy  lives  and  livelihoods.  Resilience  in  this  instance  resembles  

a  more  common  use  of  the  term  in  that  the  agency  uses  IDSS  to  build  an  America  that  is  

“ready  for”  and  “responsive”  to  threats  of  dangerous  weather.  Another  valence  reflects  

threats  internal  to  the  National  Weather  Service  from  those  who  question  the  need  for  the  

agency  in  the  era  of  “big  weather.”  IDSS,  then,  is  a  strategy  to  bolster  the  agency’s  relevance  

and  value  in  society.  Each  valence  of  resilience  promotes  different  images  of  the  forecaster  

and  who  the  forecaster  ought  to  be  in  relationship  to  their  professional  identity,  their  

scientific  enterprise,  and  their  commitments  to  different  publics.    

  I  suggest  that  IDSS  also  has  the  potential  to  dramatically  reshape  the  future  role  of  

the  National  Weather  Service  forecaster  in  society.213  Asking  normative  questions  about  

resilience  and  its  instantiations  in  forecaster  practices  and  its  implications  for  their  local  

communities  allows  me  to  reveal  alternative  ethics  that  might  reshape  a  sociotechnical  

imaginary214  of  the  forecaster,  especially  those  that  better  align  with  their  agency’s  

imperative  to  protect  lives.  Like  other  STS  scholars  who  reveal  the  ethical  and  normative  

dimensions  of  technologies,  215  I  believe  the  ethics  of  intervening  sits  alongside  the  

analyst’s  assessment  of  those  scientific  or  technological  endeavors  examined.  In  this  effort,  

213  There  is  an  important  distinction  in  the  forecasting  community  between  public  forecasters  employed  by  the  U.S.  government  to  collect  meteorological  information  and  issue  free  products  and  warnings  and  private  sector  forecasters  who  work  in  industry  and  charge  a  fee  for  their  services.  My  work  focuses  only  on  the  former,  though  an  assessment  of  the  later  would  be  welcomed  and  is  much  needed  to  understand  the  intersectionalities  of  their  practices  and  missions.  214  Jasanoff,  “Imagined  and  Invented  Worlds.”  215  Heidegger,  “The  Question  Concerning  Technology”;  Doppelt,  “What  Sort  of  Ethics  Does  Technology  Require?”;  Jonas,  “Technology  and  Responsibility”;  Jasanoff,  “Imagined  and  Invented  Worlds.”  

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I  perform  what  Downey  and  Zuiderent-­‐Jerak  call  “meta-­‐activism.”216  To  this  end,  I  offer  the  

National  Weather  Service  a  normative  suggestion  for  how  I  believe  they  might  move  

forward  in  its  pursuit  of  a  new  role  for  the  forecaster  in  the  21st  Century.  In  particular,  I  

posit  that  the  National  Weather  Service  scale  up  and  make  visible217  an  image  of  the  

forecaster  bound  by  a  new  hybrid  ethic  that  I  call  empathetic  accuracy.  It  is  an  ethic  that  

performs  the  interconnectedness  of  accuracy,  care,  and  relationality  already  present  and  

important  to  forecaster  work.    

  Why  examine  an  initiative  like  IDSS  and  why  now?  Scholars  from  fields  that  

comprise  Science  and  Technology  Studies  (STS),  have  long  argued  that  nascent  

sociotechnical  systems  flex  and  shift  as  their  activities  and  practices  are  negotiated  among  

many  relevant  social  groups.218  IDSS  is  no  exception.  Not  yet  a  standardized  practice,  it  is  

malleable  and  thus  open  to  critique  and  redesign—a  clear  reason  to  engage  with  it  at  this  

time.  However,  as  Winner  notes,  it  is  not  just  the  processes  leading  to  closure  that  should  

occupy  the  analyst’s  efforts.  Instead,  our  work  can,  and  should,  pay  attention  to  the  

consequences  of  such  practices,  the  possibilities,  and  the  invisible  and  silent  groups  who  

have  no  say  in  its  production.219  In  the  case  of  IDSS,  I  suggest  the  new  focus  on  a  limited  

group  of  “core  partners”  disappears  the  lay  public,  leaving  them  to  develop  relationships  

216  Downey  and  Zuiderent-­‐Jerak,  “Making  and  Doing:  Engagement  and  Reflexive  Learning  in  STS.”  217  Downey,  The  Machine  in  Me:  An  Anthropologist  Sits  Among  Computer  Engineers;  Downey  and  Dumit,  “What  Is  Engineering  Studies  for?:  Dominant  Practices  and     Scalable  Scholarship.”  218  Ravetz,  Scientific  Knowledge  and  Its  Social  Problems;  Hughes,  “The  Evolution  of  Large  Technological  Systems”;  Bijker,  Hughes,  and  Pinch,  The  Social  Construction  of  Technological  Systems:  New  Directions  in  the  Sociology  and  History  of  Technology;  Latour,  Reassembling  the  Social:  An  Introduction  to  Actor-­‐Network-­‐Theory.  219  Winner,  “Upon  Opening  the  Black  Box  and  Finding  It  Empty:  Social  Constructivism  and  the  Philosophy  of  Technology.”  

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with  forecasters  largely  by  proxy  through  their  public  safety  officials  or  perhaps  not  at  all.  

  This  article  is  based  on  ethnographic  and  historical  work  I  conducted  at  three  

National  Weather  Service  forecast  offices  and  my  participation  as  a  facilitator  and  subject  

matter  expert  for  eight  IDSS  webinars.  The  agency  created  this  online  platform  as  an  

internal  forum  for  agency  meteorologists  to  explore  and  negotiate  the  definitions  and  

boundaries  of  IDSS.  In  what  follows,  I  give  an  overview  of  the  National  Weather  Service  and  

the  concept  of  resilience;  I  then  offer  and  overview  of  the  Weather  Ready  Nation  Roadmap  

and  three  classifications  of  IDSS  activities  that  differently  deploy  resilience;  next  I  highlight  

the  external  and  internal  inflections  of  resilience  in  the  NWS;  finally,  I  conclude  by  arguing  

for  an  empathetic  accuracy  as  articulated  in  new  readings  of  IDSS  and  possible  changes  in  

education,  training,  and  collaborations  with  social  scientists.  

  The  National  Weather  Service  and  the  Concept  of  Resilience  

  Whenever  hazardous  weather  is  likely  to  occur  somewhere  in  the  United  States,  

operational  meteorologists  located  in  one  of  122  local  National  Weather  Service  forecast  

offices  across  the  country  assess  predictive  information;  create  a  host  of  “products”  that  

give  spatial,  temporal,  and  explanatory  details  about  what  might  happen;  and  provide  what  

is  called  “services”  (sometimes  in  the  form  of  a  product)  to  stakeholders  and  partners  in  

their  community.  During  the  immediate  minutes  during  which  storms  form  and  weather  

hazards  emerge,  NWS  forecasters  create  specific  products,  called  warnings,  which  are  

alerts  that  not  only  classify  and  demarcate  a  weather  threat  but  that  give  advice  on  actions  

people  should  take.    Since  1870,  these  activities  have  been  a  part  of  the  agency’s  mission  “to  

protect  lives  and  property,”  an  ethical  commitment  that  reflects  their  primary  

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responsibility  for  issuing  public  weather  warnings  in  the  United  States.220      

  National  Weather  Service  warnings  also  represent  an  administrative  and  public  

demonstration  of  forecasting  skill,  which  the  agency  measures  based  on  the  correctness  of  

the  prediction’s  timing,  location,  threat  type,  and  severity.    In  1999,  a  National  Research  

Council  report  suggested  that  skill  is  a  “quantifiable  element  of  the  forecast  that  

contributes  to  its  accuracy,”  one  that  allows  for  systematic  comparison  among  forecasters  

and  over  time.  Accuracy,  in  this  case,  “refers  to  the  general  or  unspecified  predictive  value  

of  a  forecast  or  forecasting  method.”221  That  is,  accuracy  is  important  to  forecasting  only  if  

someone  is  able  to  use  the  prediction.  In  many  ways,  then,  accuracy  has  functioned  as  a  

dominant  principle  in  the  agency,  motivating  its  infrastructural,  sociotechnical,  and  

professional  developments  as  evidenced  by  its  appearance  as  a  priority  goal  in  agency  

strategic  plans.222  Radar  and  satellite  networks,  real-­‐time  observational  instruments,  

forecaster  workstations,  computer  databases  and  computer  models—together  these  

instantiate  promises  of  accuracy  to  “improve”  or  “advance”  warnings  and  drive  

Congressional  funding,  policy  changes,  and  forecaster  practices.223    

  An  emphasis  on  public  service  and  decision  support  exists  in  agency  reports,  as  well,  

though  it  is  less  visible  and  more  ambiguous.  For  example,  in  the  2005  strategic  plan,  one  

220  National  Oceanic  and  Atmospheric  Administration,  “History  of  the  National  Weather  Service.”  221  National  Research  Council,  “A  Vision  for  the  National  Weather  Service:  Road  Map  for  the  Future,”  12.  222  “Weather  Ready  Nation:  NOAA’s  National  Weather  Service  Strategic  Plan”;  National  Weather  Service,  “Vision  2005:  National  Weather  Service  Strategic  Plan  for  Weather,  Water,  and  Climate  Services,  2000  -­‐  2005”;  National  Weather  Service,  “Working  Together  to  Save  Lives:  National  Weather  Service  Strategic  Plan  for  2005-­‐2010.”      223  Committee  on  the  Assessment  of  the  National  Weather  Service’s  and  Committee  on  the  Assessment  of  the  National  Weather  Service’s,  “Weather  Services  for  the  Nation:  Becoming  Second  to  None”;  The  Weather  Service  Modernization  Act  of  1992.  

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paragraph  in  twenty-­‐four  pages  of  text  explicitly  highlights  users  of  weather  information:  

“As  we  focus  on  improving  our  services  and  expanding  their  scope,”  the  report  notes,  “we  

will  consult  effectively  with  all  who  are  affected  by  our  services  and  be  guided  by  our  

customer’s  needs.”224  As  with  other  plans  before  Weather  Ready  Nation,  this  reference  to  

the  agency’s  public  is  often  stated  without  much  explanation  of  which  resources  will  foster  

and  enable  these  relationships.  Yet  a  variety  of  publics  are  the  ones  determining  the  

prediction’s  value.  In  a  chart  that  correlates  the  strategic  plan  outcomes  with  forecaster  

activities,  for  example,  a  list  of  technologies,  such  as  radar  and  observation  systems,  

constitute  the  mechanisms  for  creating  customer  service.  Thus,  developing  better,  accurate  

technologies  and  more  robust  infrastructures  may  stand  in  for  relationships  with  their  

users,  or  “customers,”  as  they’re  frequently  called.  In  this  sense,  National  Weather  Service  

publics  are  cast  as  members  in  a  business  transaction  rather  than  as  participants  in  the  

enterprise  of  protecting  lives.  

  As  with  many  kinds  of  disasters,  those  that  involve  weather  become  a  moment  of  

possible  transformation  for  institutions.    In  2011,  over  600  people  died  in  two  tornado  

disasters  in  the  United  States.  The  first  struck  six  states  in  the  Southeast  on  April  27,  

producing  363  tornadoes  and  340  deaths.225  Dubbed  the  Super  Outbreak,  it  was  followed  

the  next  month  by  another  outbreak  in  the  Midwest,  which  produced  an  EF-­‐5  tornado  in  

Joplin,  Missouri  on  May  22.  This  single  but  deadly  tornado  ripped  through  the  town  on  a  

Sunday  afternoon,  destroyed  25%  of  the  town  and  killed  159  people,  the  deadliest  single  

224  National  Weather  Service,  “Working  Together  to  Save  Lives:  National  Weather  Service  Strategic  Plan  for  2005-­‐2010,”  6.  225  National  Weather  Service,  “The  Historic  Tornadoes  of  April  2011.”  

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tornado  since  1948.226  Death  tolls  like  these  had  not  occurred  from  a  tornado  since  

the1940s,  well  before  current  warning  infrastructures  had  been  put  in  place.  Questions  

posed  by  the  weather  community  in  the  wake  of  these  disasters  centered  on  a  problematic  

of  the  public:  How  could  so  many  die  when  the  technology  and  science  of  meteorology  has  

advanced  as  much  as  it  has?  Why  didn’t  more  people  act  on  warnings?  What  could  be  done  

to  better  communicate  weather  risks?  

  In  light  of  such  troubling  problematics,  the  National  Weather  Service  launched  its  

“Weather  Ready  Nation”  strategic  plan  in  late  2011  and  a  more  detailed  “Weather  Ready  

Nation  Roadmap”  in  2013  to  help  execute  this  vision.227  The  Roadmap  is  a  75-­‐page  

document  whose  cover  design  includes  two  disconnected  puzzle  pieces  superimposed  with  

images  of  people  in  a  snowstorm  walking  on  the  street,  holding  umbrellas.  Behind  this  

image  is  a  faint  watermark  of  a  towering  storm  cloud,  positioned  as  a  threat  looming  

behind  those  in  the  puzzle  pieces.  From  the  outset,  then,  it  seems  that  what  follows  inside  

the  Roadmap  will  point  to  individuals  in  the  lay  public  who  are  at  risk  from  dangerous  

weather—it  is  a  problematic  to  be  solved.  To  this  end  the  following  pages  outline  

subdivided  plans  that  “describe  activities  and  milestones”  228  the  NWS  must  meet  to  

successfully  implement  its  vision  by  the  year  2020,  a  solution  that  “will  translate  the  

Strategic  Plan  into  real-­‐life  actions  that  save  lives  and  livelihoods.”229  

  Resilience:  Generally  and  Locally  

  A  primary  function  of  the  Roadmap,  the  authors  write,  is  to  articulate  a  plan  to  build   226  National  Oceanic  and  Atmospheric  Administration,  “NWS  Central  Region  Service  Assessment:  Joplin,  Missouri,  Tornado-­‐-­‐May  22,  2011.”  227  Furgione,  Weather  Ready  Nation  and  Social  Sciences.  228  National  Weather  Service,  “Weather  Ready  Nation  Roadmap,”  1.  229  U.S.  Department  of  Commerce,  “NOAA  Strategic  Priority:  Building  a  Weather-­‐Ready  Nation.”  

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“increasing  community  resilience  for  future  extreme  events.”  230  But  what  is  meant  by  

resilience  generally  and  within  the  pages  of  the  Roadmap  specifically?    

  On  the  larger  scale,  the  politics  of  climate  change,  climate  engineering,  and  more  

recently,  debates  about  the  anthropocene,  expose  the  mutual  constitution  of  sociotechnical,  

ideological,  and  imaginative  practices  about  the  future  of  human  survival  and  the  quality  

and  equality  of  life  on  a  warming  planet.231  As  a  science  of  uncertainty,  climate  research  

builds  on  a  scale  that  attracts  collective  attention  from  scholars,  activists,  politicians,  and  

scientists  in  the  global  north  and  south.  In  particular,  strategies  for  mitigating  and  adapting  

to  climate  change  come  to  the  foreground,  with  an  increasing  attention  to  environmental  

and  infrastructural  resilience,  in  light  of  inequitable  societal  vulnerabilities.232  Concepts  

like  these,  however,  are  not  stable  nor  unanimous  in  their  meanings  or  consequences  and  

so  shift  in  how  they  are  used,  depending  on  the  contexts  of  their  deployment.      

  The  relationship  between  resilience  and  vulnerability  is  particularly  complex  and  

fraught.  Traveling  out  of  systems  ecology  in  the  1970s,  through  various  disciplines—

human  geography,  psychology,  hazards  literature,  and  disaster  studies—and  into  

engineering  and  community  planning,  resilience  arrives  at  each  destination  ambiguous  and  

multiple.233  Current  framings  typically  cast  resilience  as  a  positive  notion,  one  that  

230  National  Weather  Service,  “Weather  Ready  Nation  Roadmap,”  5.  231  Lövbrand,  Stripple,  and  Wiman,  “Earth  System  Governmentality”;  Haraway,  “Anthropocene,  Capitalocene,  Chthulucene”;  Clark,  “Geo-­‐Politics  and  the  Disaster  of  the  Anthropocene”;  Hamilton,  “Ethical  Anxieties  about  Geoengineering”;  Humphreys,  “Smoke  and  Mirrors”;  Hulme,  Can  Science  Fix  Climate  Change?  A  Case  against  Climate  Engineering.  232  Cutter,  Hazards  Vulnerability  and  Environmental  Justice;  Lavell  et  al.,  “Climate  Change”;  Bankoff,  Frerks,  and  Hilhorst,  Mapping  Vulnerability;  Blaikie  et  al.,  At  Risk;  Dilling  et  al.,  “The  Dynamics  of  Vulnerability:  Why  Adapting  to  Climate  Variability  Will  Not  Always  Prepare  Us  for  Climate  Change.”  233  Miller  et  al.,  “Resilience  and  Vulnerability:  Complementary  or  Conflicting  Concepts?”;  Endress,  “The  Social  Constructedness  of  Resilience.”  

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proposes  institutions,  communities,  buildings,  ecosystems,  people,  and  the  like,  should  be  

altered  to  resist  or  adapt  to  disruptions  from  stressors  and  rebound  to  a  previously  

“normal”  conditions  or  equilibrium  quickly.  Within  a  system,  those  that  face  the  greatest  

losses,  express  an  inability  to  adapt  or  recover,  or  are  most  susceptible  to  harm  are  

considered  vulnerable  and  in  need  of  strategies  of  resilience.  Vulnerability,  then,  is  a  

“relational  notion”234  to  resilience,  one  likewise  arising  in  the  1970s  in  disaster  contexts  to  

highlight  those  least  capable  of  performing  flexibility  or  sites  where  weaknesses  in  the  

system  might  leave  it  open  to  disruption.    Together  vulnerability  and  resilience  constitute  

an  anticipatory  philosophy,  one  of  expectation  of  worst-­‐case  scenarios,  though  just  which  

threats  one  should  prepare  for  and  when  the  work  of  resilience  should  complete  is  

unclear.235  As  such  they  also  represent  a  crisis  framework,236  one  that  has  normative  

implications,  which  authors  Healy  and  Mesman  (2014)  suggest  get  “overlooked”  amid  the  

“imprecision”  of  its  ambiguity.  In  the  high  stakes  contexts  of  disaster  work  failures  to  

grapple  with  the  ways  vulnerability  and  resilience  are  constructed  and  their  consequences  

may  result  in  body  counts.    

  One  such  construction  is  the  difference  between  climate  change  and  weather  

discourses  that  might  affect  deployment  of  term  like  resilience,  a  difference  that  is  not  

surprising  given  that  the  separation  between  the  two  sciences  is  tenuous  and  political.  

Strictly  defined,  weather  is  the  atmospheric  processes  and  resulting  phenomena  that  

materialize  locally  on  shorter  time  scales  (e.g.  today  through  this  week,  or  from  the  next  

few  minutes  through  the  next  seven  days);  climate  is  represented  through  a  statistical   234  Healy  and  Mesman,  “Resilience:  Contingency,  Complexity,  and  Practice,”  155.  235  Endress,  “The  Social  Constructedness  of  Resilience.”  236  My  thanks  to  Dr.  Saul  Halfon  for  this  conceptualization  of  resilience,  which  highlights  the  “eventness”  and  urgency  of  weather  hazards.  

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analysis  of  the  globe  conveyed  in  a  temporality  greater  than  thirty  years,  though  it  may  

stretch  over  several  thousand  years  into  the  future  or  even  into  the  past  (e.g.  

paleoclimatology).237  Yet  they  are  intimately  related,  too.  As  Paul  Edwards,  noted  historian  

of  science,  suggests:  “Climate  knowledge  is  knowledge  about  the  past.  It’s  a  form  of  

history—the  history  of  weather…”238  

  In  the  meteorological  community,  weather  prediction  is  the  domain  of  operational  

and  broadcast  meteorologists  who  forecast  the  nuanced  variables  of  what  will  affect  people  

locally  in  their  day-­‐to-­‐day  living;  climate  prediction  involves  researchers  who  simulate  

possible  futures  at  large  temporal  and  spatial  scales  to  study  trends  and  patterns  of  

variation  for  the  planet.  While  NWS  meteorologists  collect  climate  data—daily  

observations  that  comprise  climate  reports  that  feed  into  larger  systems  of  inquiry—

among  these  government,  private,  and  broadcast  meteorologists  there  is  tension  over  the  

cause  of  climate  change,  or  global  warming.239  Nor  does  consensus  exist  on  how  or  whether  

one  can  link  extremes  in  weather  to  evidence  of  climate  change.  Weather,  then,  is  seen  as  

largely  apolitical  and  weather  events  as  acts  of  God;  climate  change  is  politics  all  the  way  

down.  

  In  terms  of  resilience,  then,  suggestions  about  how  to  deploy  the  term  in  climate  

change  scenarios  and  weather  disasters  differ  on  multiple  levels,  primarily  timescales  and  

237  See  the  following  for  a  comprehensive  explanation  of  the  differences  and  how  they  arose:  Anderson,  Predicting  the  Weather:  Victorians  and  the  Science  of  Meteorology;  Edwards,  A  Vast  Machine:  Computer  Models,  Climate  Data,  and  the  Politics  of  Global  Warming;  Fleming,  Fixing  the  Sky.  238  Edwards,  A  Vast  Machine:  Computer  Models,  Climate  Data,  and  the  Politics  of  Global  Warming,  xvii.  239  Stenhouse  et  al.,  “Meteorologists’  Views  About  Global  Warming:  A  Survey  of  American  Meteorological  Society  Professional  Members.”  

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lasting  effects.240  Climate  change  is  often  thought  of  as  a  “slow  disaster,”  for  example,  

unfolding  on  a  timeline  of  decades  if  not  centuries.241  It  likewise  brings  with  it  a  deeper  

uncertainty  about  the  types  of  transformations  one  might  expect  not  just  locally  but  on  

multiple  scales.    Resilience  in  climate  change  contexts,  then,  necessitates  identifying  the  

global  and  local  alterations  that  most  likely  represent  a  future  earth.242  Because  no  one  

scenario  can  be  demonstrated  to  represent  the  “real”  future,  however,  creating  resilience  is  

also  an  activity  of  negotiating  various  scientific  claims  and  political  ideologies.  This  

includes  the  dismissive  view  that  climate  change  is  either  a  hoax  or  that  we  cannot  know  

which  kind  of  future  to  adapt  to,  as  well  as  the  decisions  abut  how  best  to  prepare.243  And  

the  dynamics  of  climate  change  shift  in  unexpected  ways,  making  preparation  much  more  

difficult  to  execute  individually.244  Instead,  climate  change  resilience  involves  entire  

communities  or  regions.245  Thus,  many  people  are  left  without  a  clear  sense  of  just  what  

they  ought  to  be  preparing  for  now  and  how  to  begin.    

  While  weather  resilience  overlaps  in  the  sense  that  we  cannot  know  just  which  

phenomena  (e.g.  rain,  snow,  tornado)  will  affect  a  community  in  a  particular  timeframe,  the   240  Edwards,  A  Vast  Machine:  Computer  Models,  Climate  Data,  and  the  Politics  of  Global  Warming.  241  Erikson  and  Yule,  A  New  Species  of  Trouble:  Explorations  in  Disaster,  Trauma,  and  Community;  Nixon,  “Slow  Violence,  Gender,  and  the  Environmentalism  of  the  Poor.”  242  Lowe  et  al.,  “Does  Tomorrow  Ever  Come?  Disaster  Narrative  and  Public  Perceptions  of  Climate  Change”;  Yusoff  and  Gabrys,  “Climate  Change  and  the  Imagination.”  243  Bierbaum  et  al.,  “A  Comprehensive  Review  of  Climate  Adaptation  in  the  United  States:  More  than  Before,  but  Less  than  Needed.”;  Edwards,  A  Vast  Machine:  Computer  Models,  Climate  Data,  and  the  Politics  of  Global  Warming.  244  Dilling  et  al.,  “The  Dynamics  of  Vulnerability:  Why  Adapting  to  Climate  Variability  Will  Not  Always  Prepare  Us  for  Climate  Change”;  Field  et  al.,  “Climate  Change  2014:  Impacts,  Adaptation,  and  Vulnerability”;  Lavell  et  al.,  “Climate  Change.”  245  Dilling  et  al.,  “What  Stakeholder  Needs  Tell  Us  about  Enabling  Adaptive  Capacity:  The  Intersection  of  Context  and  Information  Provision  across  Regions  in  the  United  States”;  Jankovic,  Coen,  and  Fleming,  Intimate  Universality:  Local  and  Global  Themes  in  the  History  of  Weather  and  Climate.  

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shorter  duration  implied  in  weather  suggests  that  they  are  “fast  disasters.”246    A  tornado,  

for  example,  develops  over  the  course  of  minutes  and  generally  lasts  just  as  long.  Because  

these  phenomena  occur  regularly  and  are  visible  in  news  cycles  and  everyday  experience,  

they  are  more  tangible  and  “real.”  Preparation  for  particular  phenomena  occurs  at  an  

individual  or  community  scale,  as  responsibility  for  surviving  a  flood,  for  example,  is  often  

framed  as  a  personal  responsibility  (e.g.  through  flood  insurance,  preparedness  plans,  etc).  

Recovery  from  these  “events”  typically  takes  much  longer  than  the  time  the  weather  

phenomena  takes  to  cause  damage,  but  prediction  and  recovery—two  key  elements  of  

resilience—is  divided  among  various  actors,  with  the  former  being  the  purview  of  weather  

forecasters  and  the  latter,  the  purview  of  emergency  managers,  local  government,  and  

individual  citizens.247    

  Resilience  in  the  Weather  Ready  Nation  Roadmap  is  likewise  difficult  to  nail  down.    

The  initial  language  of  the  2011  Weather  Ready  Nation  report  shifts  the  discourse  of  

weather  warnings  from  a  preoccupation  with  creating  better,  discrete  products  to  the  

communication  and  interpretation  of  uncertainty  for  others.  It  potentially  puts  specific  

publics  at  the  center  of  forecasting,  alongside  accuracy.  While  this  shift  is  not  entirely  new,  

an  agency-­‐wide  commitment  to  focusing  more  explicitly  on  partners  and  communication  

strategies  is.  Instantiated  in  an  initiative  called  Impact  Based  Decision  Support  Services  

(IDSS),  it  is  part  of  what  Deputy  Director  Laura  Furgione  called  a  “formalization  of  this  

246  Braun,  “Sorting  Out  Disasters:  A  New  Case  Study  for  Classification  Theory”;  Knowles,  The  Disaster  Experts:  Mastering  Risk  in  Modern  America.  247  Anderson,  “Preemption,  Precaution,  Preparedness.”  

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[relationship  and  communication]  work”  in  the  practices,  activities,  and  metrics  of  

prediction.248    

  Broadly  defined,  IDSS  is  an  infrastructure  of  practices  and  people,  things  and  bodies  

that  materially  affect  the  outcomes  of  warning  strategies.  While  not  explicitly  articulated  as  

such  in  the  Roadmap,  much  of  the  document  is  a  working  out  of  the  institutional  

vocabulary  and  sociotechnical  apparatuses  that  might  lead  to  successful  implementation.  

Metrics  of  success  for  such  efforts  are  largely  absent  as  are  specific  activities  that  

forecasters  will  use  to  perform  IDSS  with  other  public  safety  officials,  which  they  call  “core  

partners,”  or  government  and  non-­‐government  safety  experts.  As  the  name  Impact  Based  

Support  Services  implies,  forecasters  will  focus  as  much  on  developing  accurate  products  

as  on  understanding  the  multiple  ways  that  local  weather  affects  people,  called  impacts.  

Support,  then,  suggests  that  forecasters  will  interpret  these  products  with  the  decision  

maker  and  the  impacts  that  concern  them  in  mind.  While  forecasters  may  assist  decision  

makers,  I  have  observed  that  they  only  go  so  far  as  to  describe  probabilities,  or  likelihoods  

for  different  scenarios,  and  articulate  aspects  of  their  skill:  their  confidence  in  a  particular  

probability,  for  example.  They  are  limited,  however,  by  official  policies  that  prohibit  them  

from  doing  more  than  “ensuring  they  understand  the  information  provided  in  […]  products  

relating  to  hazardous  weather.”249  Any  need  to  tailor  information  should  be  referred  to  the  

private  weather  industry.    

  Key  concepts  underpinning  the  concept  of  IDSS  are  highlighted  in  section  1.2  of  the  

Roadmap.  Important  elements  arise  through  an  emphasis  on  relationship  building  in  order  

to  reformulate  forecasting  practices  around  the  notion  of  impacts,  or  the  affect  of  weather   248  NWS  Vision  for  IDSS.  249  National  Weather  Service,  “NWS  Support  for  Special  Events,”  3.  

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on  people  and  structures.  This  requires  a  definitional  flexibility  and  distribution  of  

expertise  in  deciding  what  constitutes  an  impact  and  by  whom.  Thus,  forecaster,  the  report  

notes,  must  understand  “  what  and  how  weather  impacts  a  decision  from  the  core  partner’s  

perspective”  which  then  allows  them  to  communicate  their  “uncertainty  in  understandable  

terms.”  Becoming  resilient  as  an  institution,  then,  requires  forecasters  to  partially  share  

their  atmospheric  notions  of  risk  with  those  articulated  by  partners  who  transform  risk  

into  calculations  that  inform  actions.  Importantly,  “NWS  will  evolve  from  a  paradigm  where  

the  forecaster  generates  products  based  on  static  definitions  toward  a  services  model  

where  the  forecaster  works  closely  with  core  partners  to  recognize  their  needs  and  provide  

expertise  to  community  decision-­‐makers.”250  Forecasting  in  this  model  emphasizes  concern  

with  others’  needs  over  the  strict  accuracy  of  their  previous  models.    

  Weather  Ready  Nation  Roadmap:  IDSS  as  Resilience  

  The  Weather  Ready  Nation  Roadmap  articulates  elements  of  resilience  in  each  its  

four  plans:  services,  workforce,  science  and  technology,  and  business.  Each  plan  is  intended  

to  help  facilitate  Impact  Based  Decision  Support  Services,  and  through  it,  multiple  kinds  of  

resilience  and  I  will  discuss  shortly.  But  first,  I  offer  an  overview  of  the  plans  through  a  

broad  lens  of  resilience.    

  The  Services  Plan  emphasizes  the  activities  and  skills  forecasters  will  perform  that  

are  oriented  toward  user  decision  making  through  the  interpretation,  communication,  and  

improved  usefulness  of  weather  information.  Forecasters  must  “go  beyond  the  production  

of  accurate  forecasts  and  timely  warnings  and  build  in  improved  understanding  and  

anticipation  of  the  likely  human  and  economic  impacts  of  such  events.”  IDSS  is  the  

250  National  Weather  Service,  “Weather  Ready  Nation  Roadmap,”  11–15.  

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“overarching  paradigm”  from  which  the  NWS  will  deliver  its  services.  As  such,  it  becomes  

an  “obligatory  passage  point”251  in  this  plan,  helping  forecasters  anticipate  the  “new  and  

evolving  needs  in  society”252  and  users  access  information  shaped  according  to  their  risk  

“thresholds.”    Much  of  this  effort  is  aimed  at  helping  partners,  or  individuals  in  other  

government  public  safety  sectors,  make  effective  decisions.    

  In  the  section  on  the  Workforce  Plan,  the  Roadmap  highlights  the  forecaster  role  as  

in  alignment  with  the  Services  Plan  the  new  and  “evolved”  NWS.  Staff  must  be  adaptive  as  

IDSS  requirements  modify,  submit  to  new  training,  and  become  more  diverse  in  their  skills  

to  “meet  the  IDSS  vision.”    To  this  end,  workforce  adjustments  must  be  made  to  reflect  the  

changing  “American  demographic”  and  the  emergent  concepts  of  IDSS.  The  forecaster,  that  

is,  must  be  attuned  to  the  people  on  the  ground  in  order  to  better  reflect  their  challenges  

and  concerns  in  their  work.  Yet,  lists  within  the  document  mainly  emphasize  

communication  skills  and  a  host  of  technological  training  including  GIS,  computer  

modeling,  data  visualization  and  “broader  environmental  science  skills.”  Likewise,  training  

involves  physical  sciences,  “emerging  science  and  technology,”  communication,  

management  and  leadership,  and  outreach.  Nothing  is  said  about  how  forecasters  will  be  

trained  to  negotiate  relationships  or  to  understand  partner  needs.  Still,  these  

technoscientific  skills  are  meant  to  reflect  not  only  the  user  needs  but  those  of  the  agency,  

too:  “it  is  vital  that  NWS  remain  agile  to  keep  up  with  the  changing  science  and  technology  

and  remain  relevant  to  its  evolving  core  partner  and  user  requirements.”253  Together  the  

Services  and  Workforce  Plans  take  more  than  two-­‐thirds  of  the  document.   251  Callon,  “Elements  of  a  Sociology  of  Translation:  Domestication  of  the  Scallops  and  the  Fishermen  of  St  Brieuc  Bay.”  252  National  Weather  Service,  “Weather  Ready  Nation  Roadmap,”  6.  253  Ibid.,  35–36.  

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  The  last  two  plans  Science  and  Technology  and  Business  Plan  are  the  briefest  in  the  

Roadmap  but  highlight  the  scientific  authority  that  forecasters  bring  to  the  prediction  

process  and  that  become  part  of  the  justification  for  the  NWS  value.  Future  technoscience  

developments  are  focused  around  innovations  and  improvements  in  infrastructural  

elements  of  the  predictive  process,  especially  computer  models,  observing  systems,  and  

research  to  operations  to  research  (R2O2R)  test  bed  mechanisms.  An  emphasis  on  

“synchronizing  societal  impacts  and  environmental  data”  means  developing  the  most  

“precise  and  accurate  environmental  knowledge”  that  can  be  “delivered  on  demand”  and  in  

forms  that  are  “relevant  to  core  partners’  preparation,  response  and  recovery  actions.”254  

In  fact,  it  is  these  efforts,  the  reader  learns,  that  will  help  forecasters  develop  a  

“comprehensive  understand  of  the  societal  vulnerabilities”  that  make  IDSS  so  potentially  

important.    

  Together,  these  plans  build  toward  maintaining  the  status  of  the  National  Weather  

Service  as  a  valuable  government  organization.  But  it  is  the  Business  Plan  that  sets  out  

toward  this  goal  most  explicitly.  Its  objective,  the  document  says,  is  “deemed  most  

important  to  the  future  health  of  the  organization,”  a  goal  premised  on  the  values  of  

sustainability,  flexibility  and  agility,  and  an  “increase  in  value”  to  the  U.S.    Together  these  

constitute  the  business  model  for  the  coming  decade.  It  is  in  this  plan  where  IDSS  is  

articulated  most  clearly  as  a  mechanism  that  might  help  facilitate  “good  health”  for  the  

National  Weather  Service,  including  helping  the  organization  become  more  visible  in  its  

communities  through  its  partnerships.    It  is  also  in  this  plan  that  the  official  definition  of  

IDSS  is  stated:  “NWS’  provision  of  relevant  information  and  interpretative  services  to  

254  Ibid.,  48–51.  

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enable  core  partners’  decisions  when  weather,  water,  or  climate  has  a  direct  impact  on  the  

protection  of  lives  and  livelihoods.”  That  is,  IDSS  will  make  more  valuable  forecasters  

information  since  it  is  envisioned  that  it  will  directly  help  others  make  decisions  that  save  

lives.255  By  illuminating  various  concepts  key  to  each  plan,  the  Roadmap  highlights  the  

mutual  constitution  of  the  social  and  technical  in  forecasting.  It  likewise  reveals  the  

complex  negotiations  of  resilience  as  embodied  by  the  concept  of  IDSS.  

  IDSS  is  framed  in  through  these  plans  as  a  linchpin  of  success  in  a  larger  effort  to  

“evolve  the  culture  of  the  NWS.”256    Evolution,  in  fact,  is  an  important  framing  of  

resilience—the  verb  “evolve”  occurs  twenty-­‐seven  times  throughout  the  Roadmap,  

appearing  several  times  in  each  section,  indicating  a  systemic  effort  to  change.  And  

evolution  occurs  on  many  fronts,  from  implementation  of  new  tools  and  technologies,  

workforce  and  staffing  structures,  to  communication  of  information.  While  they  are  

discussed  separately  in  the  document,  the  three  are  mutually  constitutive  of  a  new  National  

Weather  Service,  one  that  is  nimble,  flexible,  and  ready  to  adapt—just  like  their  publics.  

“Local  offices  will  evolve  from  product  generators  to  expert  decision  support  resources,”  

the  authors  of  the  Roadmap  write.  “They  will  incorporate  societal  impacts  to  assist  local  

community  decision  makers  and  the  public  by  focusing  equally  on  production  and  IDSS.”  In  

effect,  through  its  effort  to  evolve,  the  agency  builds  resilience,  or  a  flexibility  to  respond  to  

outside  influences  and  changing  societal  needs.    Ideally,  creating  agency  resilience  allows  

the  forecasters  to  extend  its  benefits  to  their  publics.  But  just  which  publics  benefit  is  a  

point  I’ll  examine  shortly.    

  But  what  exactly  is  IDSS?  Just  as  the  official  definition  is  broad  and  vague,  in   255  Ibid.,  65–68.  256  NWS  Vision  for  IDSS.      

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practice,  the  concept  is  slippery,  expanding  and  contracting  as  local  forecast  offices  

describe  their  situated  understandings.  In  one  local  presentation,  for  example,  IDSS  

encompasses  a  larger  number  of  NWS  forecasting  elements.  The  slide  includes  the  

equation:  “Science/Forecast  +  Partnerships  /  Outreach  +    Operations  +  Services  Suite  +  

Training  =  IDSS  culture.  Several  practices  and  people  in  the  forecaster’s  everyday  world  

add  up  to  IDSS  culture.  Yet,  they,  themselves,  are  somehow  outside  this  equation.  This  

equation  is  accompanied  by  a  graphic  that  situates  the  word  IDSS  in  the  center  of  a  circle  

with  the  same  five  “core  components”  highlighted.  IDSS  in  this  case  centers  all  aspects  of  

the  forecasters  profession,  including  their  scientific  practices.  “IDSS  is  rooted  in  quality  

forecasts  based  on  sound  science,”  the  presenter  said  of  this  slide.  This  one  instance  

illustrates  the  multiplicity  of  definitions  and  visions  for  IDSS  and  its  relationship  to  

forecasting.    

  Nor  does  the  Roadmap  articulate  exactly  which  activities  the  agency  is  willing  to  

count  as  IDSS.  IDSS  encompasses  a  range  of  activities,  services,  and  products  offered  by  

NWS  forecast  offices  across  the  country.  From  my  participation  in  the  first  national  IDSS  

webinar  series  hosted  internally  by  the  NWS  for  its  own  employees,  I  have  observed  a  

diverse  breadth  and  scope  of  practices  that  forecasters  have  labeled  IDSS;  however,  most  

fall  into  one  of  three  loose  categories.    

  The  first  include  what  I  call  dissemination  IDSS,  or  those  that  require  the  forecaster  

to  repackage  or  explain  weather  information  for  particular  users.  The  aim  is  to  better  

communicate  expert  knowledge  of  weather  conditions  and  impacts  and  is  often  

unidirectional  by  design.  Common  examples  include  weekly  webinars  that  offer    

descriptions  of  upcoming  changes  in  weather  and  possible  effects  on  the  community  

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through  graphical  and  textual  PowerPoint  slides.  In  the  days  leading  up  to  dangerous  

weather  these  are  often  coupled  with  conference  calls  held  with  local  public  safety  officials,  

such  as  principals  of  school  district.  .      

  Another  category  includes  embedded  IDSS,  or  those  that  necessitate  a  forecaster  join  

a  partner  or  stakeholder  group  on-­‐site  to  offer  ongoing  meteorological  advice  and  updated  

predictive  information  relevant  to  others’  needs.  Perhaps  the  oldest  and  most  familiar  

example  is  the  iMet,  or  incident  meteorologist,  who  has  trained  in  fire  weather.  These  NWS  

staff  members  become  part  of  the  local  incident  command  team  and  are  tasked  with  

keeping  firefighters  safe  “by  interpreting  weather  information,  assessing  its  effect  on  the  

fire  and  communicating  it  to  fire  crews.”257  While  iMets  are  traditionally  dedicated  to  fire  

weather  contexts,  NWS  forecasters  are  increasingly  following  the  iMet  model  in  other  

hazardous  weather  situations,  inserting  themselves  in  emergency  operation  centers  and  

with  other  government  entities.    

  The  last  category  I  call  relationship  IDSS,  which  differs  from  the  last  two  in  its  

emphasis  not  on  the  meteorological  expertise  forecasters  deploy  but  on  the  needs  and  

concerns  of  the  people  with  whom  they  work.  Relationship  building  exists  in  the  other  two  

categories,  but  is  not  the  explicit  goal—it  is  an  outcome  or  byproduct  of  the  situation  and  

necessary  interactions.  Relationship  IDSS,  then,  is  not  about  giving  predictive  information  

during  or  just  before  hazardous  weather;  instead,  it  is  performed  continuously  throughout  

the  year  and  in  ways  that  allow  for  an  exchange  of  perspectives  and  a  deeper  

understanding  between  individuals  about  their  roles  and  requirements  in  the  warning  

process.  In  this  type  of  IDSS,  the  rapport  built  between  people  and  the  knowledge  of   257  National  Weather  Service,  “Eyes  on  the  Sky:  A  Day  in  the  Life  of  an  Incident  Meteorologist  (IMET)  on  the  Front  Lines  of  a  Wildfire.”  

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specific  conditions  of  their  work  shapes  the  practices  forecasters  engage  in  and  the  types  of  

predictive  information  they  produce.  It  is  this  latter  category  that  has  the  most  potential  for  

highlighting  the  multiple  ethics  that  exist  in  forecaster  practices.    

  Although  I  have  categorized  activities  to  demonstrate  that  certain  ones  are  

extensions  of  an  older  emphasis  on  accuracy  and  forecaster  authority,  for  forecasters  in  the  

IDSS  webinars  these  activities  are  fluid  in  their  practices.  As  one  forecaster  noted  in  a  

presentation,  the  type  of  activity  they  select  is  relative  to  a  request  for  IDSS  from  their  

partners  and  to  the  significance  of  the  impact  of  weather  for  their  respective  community.  In  

the  graphic  accompanying  his  talk,  a  “Pyramid  of  IDSS”  illustrated  his  point.  At  the  bottom,  

in  a  wide  green  layer,  were  the  words  “Routine  Briefings,”  which  he  explained  included  

weekly  webinars  based  on  pre-­‐formatted  PowerPoint  slides.    

  At  the  next,  thinner  green  layer,  was  “Heads  Up  Email  Support,”  or  notifications  

about  the  possibility  of  dangerous  weather  sent  out  through  a  list  serve  several  days  in  

advance;  the  middle  orange  layer  noted  “Conference  Call  and  Range  of  Possibility  

Graphics,”  which  he  said,  allowed  them  to  visually  and  orally  “communicate  and  explain  

uncertainty”  to  their  partners.  The  penultimate  layer  in  pale  orange  said,  “Video  2014,”  a  

reference  to  a  local  safety  video,  and  at  the  top  and  smallest  layer  was  “Direct  Support,”  

which  included  on-­‐site  and  remote  weather  advice  during  public  events.  Along  the  right  

hand  side  of  the  graphic  was  a  black  arrow  pointing  up,  with  the  word  “Impact”  next  to  it.  

He  explained,  they  “prioritized  their  efforts  based  on  where  the  request  fell  on  this  

pyramid.”  Impact,  however,  might  be  the  number  of  people  involved  in  an  activity  that  

required  meteorological  support  or  the  political  importance  of  an  event  (like  a  Papal  visit),  

such  that  it  might  be  considered  an  issue  of  national  security.    

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  I  would  like  to  return  to  Healy  and  Mesman  and  their  call  for  those  of  us  who  

employ  the  term  resilience  to  ask  questions  about  the  context  and  consequences  of  its  use.  

Within  the  scope  of  the  Weather  Ready  Nation  strategic  plan  and  the  activities  and  

practices  generated  by  the  IDSS  initiative,  I  suggest  that  resilience  is  inflected  primarily  in  

two  ways:  internally  toward  the  agency  and  its  staff  and  externally  toward  partners  and  

stakeholders.  Distinctions  between  the  two  are  often  muddled  in  the  execution  of  resilience  

as  the  same  practices  might  embody  both  internal  and  external  articulations  against  or  for  

something  or  someone.  It  is  a  point  made  most  apparent  in  tensions  meteorologists  express  

over  future  practices  that  in  which  they  will  engage  as  the  NWS  transforms  their  work  from  

that  which  emphasizes  accuracy  to  that  which  embodies  resilience.  

  External  Resilience:  Ready,  Responsive  and  Resilient  

  Within  the  pages  of  the  Weather  Ready  Nation  Roadmap,  the  imaginary  of  the  U.S.  

population  is  briefly  constructed  as  one  that  is  “ready,  responsive,  and  resilient:”    

NOAA’s  Next  Generation  Strategic  Plan  establishes  a  long-­‐term  goal  of  a  “Weather-­‐Ready  Nation,”  as  part  of  a  broader  vision  of  resilient  ecosystems,  communities,  and  economies.  Weather-­‐Ready  Nation  is  about  building  community  resilience  in  the  face  of  increasing  vulnerability  to  extreme  weather  and  water  events.  In  the  end,  emergency  managers,  first  responders,  government  officials,  businesses,  and  the  public  will  be  empowered  to  make  faster,  smarter  decisions  to  save  lives  and  protect  livelihoods.258  (italics  added)    

To  achieve  national  success,  the  Roadmap  builds  its  argument  for  resilience  at  the  smaller  

scale  of  local  communities,  with  specific  emphasis  on  individual  partners  and  decision  

makers;  motivation  for  this  framing  is  not  only  an  increase  in  extreme  weather  

occurrence—as  one  might  expect  in  the  discourse  of  climate  change  resilience—but  a  

growing  vulnerability  within  the  population  and  infrastructures  to  those  extremes  that  

258  “Weather  Ready  Nation:  NOAA’s  National  Weather  Service  Strategic  Plan,”  1.  

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happen  each  year.  External  resilience  might  thus  be  conceived  of  as  for  individuals,  

especially  vulnerable  individuals,  and  is  deployed  against  threats  of  dangerous  weather.  

  Just  who  or  what  is  vulnerable  and  the  mechanisms  by  which  vulnerability  ought  to  

be  identified  is  not  clearly  addressed  in  the  plan.  Vulnerability  is  an  empty  signifier  in  a  risk  

society259  where  experts  are  not  always  able  to  recognize  or  identify  who  is  likely  to  be  

most  negatively  affected  by  weather  disasters.  Expanding  the  network  of  expertise  to  

public  safety  officials,  they  suggest,  would  enable  forecasters  to  better  visualize  where  

vulnerabilities  exist  and  how  choices  might  be  made  to  reduce  loss  of  life.  The  kinds  of  

resilience  that  ought  to  be  built  and  where  is  likewise  unclear,  though  the  strategic  plan  

only  hints  at  possible  answers.  

  External  resilience  is  likewise  framed  as  a  support  mechanism  for  decision  makers  

who  are  responsible  for  taking  actions  on  behalf  of  the  lay  public.  By  building  meaningful  

relationships  with  core  partners,  NWS  forecasters  create  resilience  against  ignorance  of  the  

circumstances,  contexts,  and  needs  or  thresholds  that  need  to  be  built  into  predictive  

information.  Similarly,  these  same  core  partners—emergency  managers  and  other  

governmental  agencies,  school  districts,  hospital  administrators,  etc.—manage  resources  

that  are  important  to  public  safety.  The  assumption  is  that  together  forecasters  and  

emergency  managers  are  both  generating  resilience  against  similar  threats—weather  

hazards,  loss  of  life.  On  one  level  they  are.  Uncertainty  is  a  common  embodiment  of  

vulnerability,  a  threat  that  the  NWS  suggests  might  be  mitigated  through  better  

communication.  Often  defined  as  the  range  of  possible  weather  outcomes  and  their  

likelihoods,  including  worst-­‐case  scenarios  and  extremes,  uncertainty  shifts  the  

259  Beck,  Risk  Society:  Towards  a  New  Modernity.  

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responsibility  for  decisions  to  those  who  receive  such  information.  That  is,  only  core  

partners  actually  make  decisions  about  community  needs  and  so  bear  direct  responsibility  

for  consequences  of  their  efforts.  Thus,  Impact  Based  Decision  Support  Services,  as  one  

government  report  notes,  is  an  “enhanced,  multi-­‐disciplinary  approach  will  empower  

emergency  managers,  first  responders,  government  officials,  businesses  and  the  public  to  

make  fast,  smart  decisions  to  save  lives  and  livelihoods.”260    But  even  these  core  partners  

shift  in  practice  as  forecasters  highlight  the  people  who  do  not  neatly  fit  this  category,  such  

as  organizers  of  large  sporting  events  like  NASCAR  or  those  responsible  for  coordinating  a  

visit  by  the  Pope  to  a  large  U.S.  city.  

  Public  forecasters  explicitly  refuse  to  join  these  partners  in  making  the  decisions—

they  are  part  of  the  process  but  stop  short  of  weighing  in  on  the  actual  choice.  As  I’ve  

witnessed  many  times,  a  forecaster’s  support  of  a  decision  ends  with  providing  their  best  

accounting  of  uncertainty.  “That’s  not  my  job,”  is  a  common  response  to  public  school  

administrators’  request  for  forecasters’  advice  on  whether  or  not  to  close  schools  early  or  

release  buses  filled  with  students.  In  one  way,  then,  external  resilience  is  also  against  a  

threat  of  direct  responsibility.  How  people  choose  to  act  on  the  predictive  information  that  

forecasters  provide  is  outside  the  scope  of  their  role  in  society.  Forecasters  are  implicated  

in  decisions  that  affect  the  larger  community  through  the  clarity  of  their  explanations  of  

uncertainty,  as  well  as  their  expressions  of  confidence  for  different  ranges  of  possible  

outcomes.  

  Internal  Resilience:  Weather  Ready  Nation  or  Ready  Weather  Agency?  

260  American  Meteorological  Society,  “State  of  the  Weather  and  Climate  Enterprise,”  2.  

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  In  a  presentation  at  the  American  Meteorological  Society’s  annual  meeting  in  

January  2016,  NWS  administrators  provided  an  update  to  the  larger  community  about  the  

agency’s  Operations  and  Workforce  Analysis  conducted  by  McKinsey  &  Company.  The  

presenter  directly  connected  efforts  to  assess  their  organization  with  the  goals  of  a  

Weather  Ready  Nation  and  IDSS.    As  the  title  of  her  talk  suggested,  the  anticipated  report  

would  help  in  “Evolving  the  NWS  to  Build  a  Weather  Ready  Nation”  through  “actionable  

ideas”  that  will  change  operational  practices,  staffing  responsibilities,  and  organizational  

structure.  It  would  also  offer  a  “set  of  skills  so  we  can  continually  evolve  over  time  to  meet  

the  changing  needs  of  society.”261  This  echoes  the  Weather  Ready  Nation  report  with  steps  

in  the  agency  to  move  forecasting  toward  interpretation  and  communication.  

  Internal  resilience  might  be  conceptualized  as  one  that  strengthens  the  agency,  the  

NWS  itself,  against  the  threats  of  irrelevancy  raised  by  Congress  time  and  again  as  private  

sector  companies  threaten  to  displace  the  agency.262  Vulnerability,  then,  is  not  just  a  

characteristic  of  people  in  the  public  but  one  that  applies  to  the  health  and  vitality  of  the  

organization.  Resilience,  as  captured  in  the  praxis  of  forecasting  through  IDSS,  is  about  

building  in  flexibility  for  the  institution  such  that  it  might  evolve  to  meet  the  “changing  

needs  of  society”  in  ways  that  make  their  services  valuable.  To  meet  these  needs,  one  must  

know  them,  and  this  necessitates  a  reorganizing  and  retraining  of  the  workforce  toward  

such  ends.  Science  and  accuracy  are  still  important,  but  resilience  as  relationship  building  

is  on  equal  footing.    

261  Swanson-­‐Kagan  et  al.,  “Update  on  the  NWS  Operations  and  Workforce  Analysis.”  262  Samenow,  “Senate  Bill  Proposes  Centralizing  Weather  Service  Forecasting  in  6  Regional  Offices.”  

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  One  challenge  to  this  internal  inflection  is  the  response  by  individual  forecasters  in  

the  NWS,  many  of  whom  fear  that  IDSS  represents  a  threat  is  a  loss  of  scientific  authority  

within  their  own  enterprise  as  they  transition  to  interpreters  of  uncertainty.  In  my  

fieldwork,  several  forecasters  have  expressed  diverse  reactions  to  what  they  perceive  as  a  

loss  of  their  scientific  expertise  in  light  of  new  initiatives  like  IDSS.  As  their  traditional  

operational  duties  have  continually  been  scaled  back,  moving  them  into  the  narrower  role  

of  the  warning  expert—a  relatively  small  but  important  part  of  their  daily  operations—

forecasters  have  begun  to  ask  what  to  do  with  their  time  at  work  if  they  don’t  manage  daily  

weather  prediction.  They  are  accustomed  to  functioning  as  an  authority  in  matters  related  

to  prediction.  As  Daipha  (2015)  notes  of  this  practice,  “How  disciplined  improvisation  is  to  

be  transformed  into  a  masterful  weather  forecast  falls  under  forecasters’  sole  

responsibility  and  discretion.”263  And  it  is  in  this  accountability  for  the  accuracy  that  

forecasters  are  most  comfortable  and  where  they  find  the  passion  and  pride  in  their  work.      

  Within  a  Weather  Ready  Nation,  however,  the  role  of  the  forecaster  is  projected  to  

change  in  ways  that  have  been  met  with  skepticism  and  frustration  by  individuals.  To  shift  

their  job  from  prediction  to  communication  is  as  some  have  said  to  me,  “waste  forecasters’  

scientific  training,”  situating  them  in  the  role  of  “hand  holders”  for  their  partners  and  

“translators”  of  science.264  To  these  forecasters,  IDSS  is  a  strategy  of  resilience  for  the  

bureaucracy  who  must  justify  the  value  of  the  agency  to  Congress  and  the  public;  but  to  

their  minds,  it  sacrifices  of  their  skill  and  expertise.  The  tradeoff,  then,  is  a  devaluing  and   263  Daipha,  “From  Bricolage  to  Collage:  The  Making  of  Decisions  at  a  Weather  Forecast  Office,”  794.  264  Many  forecasters  dismiss  IDSS  and  refuse  to  participate  in  activities  that  have  an  explicit  connection  to  this  initiative.  As  of  today,  administrators  are  allowing  this  choice  with  the  belief  that  employee  turnover  and  cultural  changes  made  agency  wide  will  eventually  change  attitudes  and  behaviors.  

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deskilling  of  a  profession  and  its  members.  In  one  reading  of  the  internal  resilience,  the  

answer  to  the  question  about  just  who  resilience  is  for  seems  to  be  the  experts,  particularly  

the  NWS  forecasters  against  threats  from  their  own  institution.  

  Within  the  weather  forecasting  community,  many  believe  that  regardless  of  

implications  to  the  role  of  the  forecaster,  the  kinds  of  resiliency  efforts  suggested  in  IDSS  

practices  will  result  in  reduced  deaths  since  forecasters  will  be  better  positioned  to  

represent  their  expertise  to  the  people  who  depend  on  it.    But  will  it?  Is  building  

relationships  with  other  experts,  or  “core  partners,”  in  public  safety  enough  to  ensure  that  

the  NWS  administrators  will  not  have  to  answer  again  the  question  about  why  so  many  

people  died  in  a  disaster  in  spite  of  current  improvements  in  their  sociotechnical  

infrastructure?    

  A  tradeoff  of  internal  and  external  resilience  unexamined  in  Weather  Ready  Nation  

Roadmap  is  an  implicit  assumption  by  these  experts  is  that  an  understanding  of  partner  

needs  will  trickle  down  to  offer  protection  to  individuals  in  their  communities.  For  this  

reason,  there  is  no  “public  IDSS”  that  extends  to  the  lay  public  the  commitment  to  develop  

relationships  and  knowledge  about  the  multiplicity  and  complexity  of  individual  lives.  I  

argue  there  ought  to  be.  Those  outside  the  system  of  IDSS,  especially  those  who  rely  for  

their  safety  on  NWS  products,  may  face  new  dynamics  of  vulnerability  from  consequences  

of  resilience  deployed.  Yet  in  current  formulations  of  IDSS  they  have  been  largely  elided  

from  the  weather  forecasting  structures  that  determine  types  and  mechanisms  for  life-­‐

saving  alerts  and  the  understanding  that  comes  from  deep  relationships  built  over  time.  

Except  through  representation  of  those  in  the  public  sector  or  through  research  results  

relayed  by  social  scientists.  Thus,  bureaucratic  initiatives  like  IDSS  that  reorient  the  policies  

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and  practices  of  individual  forecasters  and  their  relationships,  attitudes,  and  actions  

toward  different  publics  embody  this  constitution  of  worlds  and  so  deserve  attention.    The  

scale  of  effects  such  practices  have  on  lives  exists  both  at  the  societal  and  institutional  level,  

but  more  importantly  in  this  case,  could  exist  at  the  individual  level  as  forecasters  from  

local  offices  interact  with  people  in  their  immediate  geographical  and  County  Warning  

Areas.265  

  One  potential  consequence  of  this  omission  is  that  several  new  warning  

infrastructures  and  tools  are  already  underway  that  mimic  the  IDSS  concept  to  some  

degree,  allowing  partners  to  give  input  in  defining  hazards.  As  some  interviewees  creating  

these  technologies  have  suggested  to  me,  these  efforts  are  an  improvement  over  old  

models  where  the  forecaster’s  needs,  criteria,  and  preferences  dictated  the  kind  of  

information  disseminated.  Still,  these  newer  efforts  continue  to  position  NWS  forecasters  

as  authorities  in  issuing  products  that  go  out  to  the  lay  public  in  formats  and  mechanisms  

untested  and  untried  by  a  number  of  their  various  publics.    There  is  no  facilitation  of  

comprehensive  and  diverse  input  into  the  kinds  of  weather  hazards  and  severity  levels  that  

affect  individual  lives—or  how  forecaster  practices  might  better  reflect  these  needs.  

Instead  of  embedding  values  derived  from  relationships  with  their  communities,  then,  

these  systems  re-­‐inscribe  forecaster  roles  with  only  a  gloss  of  a  changed  communication  

strategies  and  only  a  partial  fulfilling  of  their  mission  to  protect  all  members  of  the  public.    

 

Conclusion:  The  Emergence  of  an  Empathetic  Accuracy      

265  County  Warning  Areas  are  official  jurisdictions  managed  by  each  Weather  Forecast  Office.  In  general,  these  CWAs  were  decided  based  on  the  scope  of  radar  coverage  in  the  1980s  and  they  cross  statutory,  political,  and  topographical  boundaries.  

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  Leadership  within  the  NWS  note  that  they  have  dispersed  “change  agents”  

throughout  the  agency  to  evolve  the  agency  toward  a  “weather  readiness”  and  carry  out  a  

new  “operational  philosophy”  that  explicitly  rejects  a  priority  on  accuracy  as  a  mechanism  

for  the  future  in  lieu  of  a  priority  on  “deep  relationships.”266  These  individuals  are  likely  

involved  at  the  regional  and  local  levels  in  helping  to  develop  practices  in  concert  with  

IDSS,  the  initiative  that  most  clearly  has  the  potential  to  allow  forecasters  a  meaningful  

engagement  with  their  core  partners.  Still,  many  of  the  ways  that  IDSS  has  begun  to  evolve  

do  not  yet  reflect  the  kinds  of  ethic  necessary  to  be  truly  transformative.  Currently,  

forecasters  tend  to  substitute  accuracy  for  the  goal  of  serving  people.267  As  scientific  

experts,  NWS  meteorologists  would  need  to  create  a  profession  trained  and  educated  not  

only  to  be  caretakers  of  accuracy  but  to  be  caretakers  of  the  people  they  serve,  as  well.  

These  two  lines  of  care,  however,  are  not  mutually  exclusive.    

  My  critical  participation  in  the  weather  community  over  the  past  five  years  has  

compelled  me  to  take  seriously  a  normative  obligation  to  identify  problematics  important  

to  forecasters  and  dominant  images  in  of  themselves  within  the  forecast  community.268  For  

in  shaping  how  forecasters  see  themselves  and  others,  I  am  joining  them  in  their  

commitment  to  help  protect  lives  by  creating  a  system  that  reflects  the  needs  and  concerns  

of  people  in  harm’s  way.  Like  others  who  have  followed  this  normative  turn  in  STS,269  I  

believe  my  analysis  ought  to  reveal  to  those  with  whom  I  participate—and  to  those  who  

266  U.S.  Department  of  Commerce,  “Evolution  of  the  National  Weather  Service.”  267  Morss,  “Problem  Definition  in  Atmospheric  Science  Public  Policy:  An  Example  of  Observing-­‐System  Design  for  Weather  Prediction.”  268  Downey  and  Dumit,  “What  Is  Engineering  Studies  for?:  Dominant  Practices  and     Scalable  Scholarship”;  Downey  and  Zuiderent-­‐Jerak,  “Making  and  Doing:  Engagement  and  Reflexive  Learning  in  STS.”  269  Cohen  and  Galusky,  “Guest  Editorial.”  

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join  me  in  disaster  STS  activist  research—alternatives  that  reframe,  scale  up,270  and  

reformulate  future  alternative  roles  for  themselves  and  their  practices  in  society.  That  is,  I  

view  my  activism  as  one  of  studying  up271  in  order  to  help  NWS  warning  practices  become  

more  attuned  to  and  successful  at  meeting  the  agency’s  ethical  obligation  to  protect  lives.  

  To  this  end,  I  offer  the  term  “empathetic  accuracy”  as  a  way  to  reframe  what  seems  

like  a  binary  in  forecaster  work  to  better  reflect  the  complex  practices  and  relational  

negotiations  they  encounter  in  their  daily  practices.  I  define  the  term  as  a  hybrid  ethic  that  

imbricates  care,  resilience  and  accuracy  in  the  practices,  attitudes,  and  materials  

technoscientific  experts  develop  for  their  publics.    That  is,  empathic  accuracy  provides  

forecasters  with  an  ethic  that  focuses  on  predictive  precision  through  their  commitment  to  

a  relational  ethic  with  their  publics.  Based  on  principles  underpinning  Virginia  Held’s  ethic  

of  care,272  empathetic  accuracy  calls  attention  to  the  ways  these  values  are  imbricated  in  

their  science,  co-­‐constituted  in  their  technological  developments  and  policies,  and  reflected  

in  the  activities  the  direct  their  interactions  with  different  publics.  It  is  an  ethic  that  

highlights  relationships  among  people  and  the  work  of  meeting  the  needs  of  others  as  

central  to  existence.    In  this  sense,  this  hybrid  ethic  is  not  far  off  from  the  direction  the  

National  Weather  Service  says  it  wants  its  enterprise  to  go.  Yet  this  ethic  goes  further.  It  

makes  public  that  desire,  revealing  in  a  new  dominant  image273  of  the  forecaster  

relationships  as  a  central  premise  to  forecasting  science  itself  and  forecasting  practices  in  

particular.   270  Downey  and  Dumit,  “What  Is  Engineering  Studies  for?:  Dominant  Practices  and     Scalable  Scholarship.”  271  Nadar,  “Up  the  Anthropologist:  Perspectives  Gained  from  Studying  up”;  Priyadharshini,  “Coming  Unstuck:  Thinking  Otherwise  about  ‘Studying  Up.’”  272  Held,  The  Ethics  of  Care:  Personal,  Political,  and  Global.  273  Downey,  The  Machine  in  Me:  An  Anthropologist  Sits  Among  Computer  Engineers.  

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  This  suggestion  to  combine  accuracy  and  care  is  not  entirely  new.  In  fact,  an  

etymology  of  accuracy  in  early  sixteenth  century  Latin  is  accuratia  or  care  or  attention,  as  

in  “executed  with  great  care.”274  Perhaps  somewhat  fortuitously,  then,  the  linguistic  root  of  

the  term  accuracy  involves  care.  Yet  accuracy  as  a  term  is  overly  burdened  by  scientized  

connotations  about  precision,  truth,  and  objectivity,  that  its  difficult  to  merely  point  to  its  

roots  and  have  that  suffice  as  a  way  of  enrolling  forecasters  to  consider  care  as  an  

important  ethic  in  their  labor.  

  So  what  would  a  world  of  forecasting  look  like  should  it  scale  up  empathetic  

accuracy?  In  the  short  term,  I  suggest  it  would  include  immediate  and  systemic  

transformations  in  the  education  and  training  of  forecasters.  Then  they  might  have,  from  

the  very  beginning  of  their  careers,  skills  that  match  the  expectation  of  a  job  based  on  both  

on  meteorological  knowledge  and  public  service  and  care.  They  might  model  their  curricula  

after  those  in  medical  professions  that  synthesize  the  two.  A  diversity  of  coursework  would  

include  communication,  for  example,  but  not  just  that.  Future  forecasters  would  take  

classes  in  the  sociocultural  and  historical  contingencies  of  meteorology,  for  example,  spend  

time  grappling  with  the  philosophical  and  ethical  implications  of  their  profession,  and  learn  

more  about  the  vulnerabilities  that  people  face  through  on  the  ground  volunteer  work  and  

internships.  In  short,  their  education  and  training  should  help  them  be  more  engaged  in  

practices  of  care  that  are  in  line  with  these  new  job  descriptions  and  expectations  for  

knowing  how  best  to  communicate  with  others.  

  In  a  longer  term,  deploying  an  empathetic  accuracy  could  remake  forecasters’  

relationships  with  a  variety  of  publics.  Rather  than  a  largely  invisible  enterprise  that  sees  

274  “Accuracy.”  

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itself  as  autonomous  in  its  metrics  and  systems  for  accounting  for  success,  the  profession  

and  agencies  of  forecasting  might  engage  their  publics  more  directly  and  visibly.  They  

could  hold  public  debates,  enjoin  different  populations  to  offer  input  on  technological  

developments,  and  invite  the  communities  to  help  them  discover  what  science  has  

remained  “undone”275  and  thus,  perhaps  unintentionally,  contributes  to  elements  of  their  

work  that  facilitates  the  unjust.  Vulnerabilities,  for  example,  might  be  recognized  and  

addressed  more  quickly  in  their  practices  and  missing  publics  might  be  identified  and  

made  visible.  

  This  alternative  world  might  also  lead  to  a  greater  sense  of  shared  responsibility  for  

public  safety,  where  forecasters  and  core  partners  are  less  segmented  in  their  decisions  

about  public  safety  A  Weather  Ready  Nation,  then,  might  not  be  such  a  fragmented  one,  

with  strict  boundaries  of  politics  driven  by  a  political  economy  of  prediction;  instead,  to  be  

weather  ready  would  entail  a  unified  effort,  more  in  keeping  with  those  narratives  of  

climate  change  that  challenge  clear  distinction  between  public  and  expert  in  a  world  in  

which  everyone,  like  Beck’s  risk  society,276  is  equally  affected.    

  Finally,  a  new  kind  of  science  might  emerge  from  this  ethic,  one  that  takes  more  

seriously  the  commitment  to  understanding  individual  needs.  It  would  be  a  science  in  

keeping  with  the  strong  objectivity  of  feminist  scholars  who  argue  that  better  science  

comes  from  below,  from  a  multiplicity  of  standpoints,  and  by  beginning  the  enterprise  from  

the  people  on  the  margins.  As  Sandra  Harding  points  out,  “The  scientific/epistemological  

275  Frickel  et  al.,  “Undone  Science:  Charting  Social  Movement  and  Civil  Society  Challenges  to  Research  Agenda  Setting.”  276  Beck,  Risk  Society:  Towards  a  New  Modernity.  

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and  ethical/political  are  inseparable  in  standpoint  approaches  to  research.”277  The  science  

is  remade  from  the  outside  in,  with  diverse  perspectives  blurring  lines  between  expert  and  

public.    It  is  through  these  kinds  of  revisions,  I  suggest,  that  the  forecasters’—that  our—

ethical  commitments  to  society  can  best  be  fulfilled.  

  In  this  article,  I  have  focused  much  of  my  critique  on  revealing  that  which  may  be  

invisible  to  the  weather  community  in  concept  of  resilience  as  it  is  framed  in  their  Weather  

Ready  Nation  initiative  and  their  IDSS  paradigm.  And  I  have  argued  that  the  concept  of  

resilience  based  on  deep  relationships  with  others  offers  the  most  promising  way  forward  

for  NWS  forecasters  in  meeting  their  mission  to  protect  lives—if  it  accounts  for  the  

multiple  valences  and  possible  omissions  such  a  term  possesses.  But  it  does  not  yet  go  far  

enough.  Resilience  framed  as  deep  relationships  with  multiple  publics,  from  core  partners  

to  everyday  citizens,  retains  potential  as  a  productive  concept  for  a  weather  community  to  

uphold.  Recognizing  the  problematic  ways  that  current  conceptualizations  of  resilience  

functions  in  the  infrastructure  is  a  first  step  toward  revising  the  system.      

   

277  Harding,  “Standpoint  Theories:  Productively  Controversial,”  193.  

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Article  4:  Compromise  and  Action:  Tactics  for  Doing  Ethical  Research  in  Disaster  Zones  

 

Prologue  

   

  Collaborative  writing  is  common  among  disciplines  in  the  social  sciences,  especially  

where  journal  articles  are  the  primary  currency  of  academia  and  funding  agencies.  Yet  in  

the  humanities,  single  authored  monographs  still  carry  the  most  weight  and  are  thus  the  

standard.  As  a  field,  STS  straddles  humanities  and  social  sciences,  with  faculty  members  

engaging  in  both  kinds  of  publication  types.  Yet  students  are  often  trained  through  

coursework  and  the  dissertation  to  only  engage  in  solo  publication  practices  as  part  of  the  

credentialing  for  the  Ph.D.  Even  if  students  felt  there  were  flexibility  in  collaborative  

writing  for  the  dissertation  as  there  is  in  other  disciplines,  the  challenges  of  learning  how  to  

publish  are  significant  for  those  who  never  have  before,  and  finding  collaborators  who  

understand  the  process  and  are  willing  to  work  with  a  novice  scholar  can  be  even  more  

daunting.  

  Since  there  are  few  people  in  the  department  who  study  what  I  do  or  who  are  

interested  in  co-­‐publishing  based  on  my  interests  in  weather  disasters,  I’ve  had  to  look  

elsewhere  for  writing  partners.  One  strategy  I’ve  found  is  to  build  relationships  with  

mentors  in  the  meteorological  and  disaster  community  who  might  be  interested  in  having  

me  conduct  research  and  collaborate  with  them  on  publications.  First,  of  course,  is  the  

research  community  at  NCAR  where  I  completed  a  Graduate  Student  Visiting  program  in  

2014-­‐2015.    I  didn’t  apply  to  this  program  blindly,  however.  I’d  been  building  relationships  

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with  a  few  people  there  since  2012  when  I  met  them  at  meteorological  conferences.  

Reading  their  work  and  learning  about  the  kinds  of  research  they  did  confirmed  for  me  that  

I  could  be  a  good  fit  for  collaborative  work.  Since  my  time  ended  there,  they’ve  generously  

let  me  continue  working  with  them  on  a  NSF  funded  project  called  CHIME  (Communicating  

Hazard  Information  in  the  Modern  Environment:  http://www2.mmm.ucar.edu/chime/).  

To  date,  I’ve  been  included  as  one  of  multiple  authors  on  a  few  sub-­‐projects  within  the  

larger  group  of  physical  and  social  scientists  and  through  this  experience  I’ve  learned  a  

great  deal  not  only  about  collaborative  writing  but  collective  research  processes.    

  Another  collaboration  happened  in  a  more  spontaneous  way.  I  first  met  Max  

Liboiron  at  my  first  4S  meeting  in  Copenhagen  in  2011.  Sumitra  Nair  acted  as  a  mentor  of  

sorts  for  me  at  this  conference  and  introduced  me  to  many  of  her  STS  connections.  A  year  

later,  Hurricane  Sandy  struck  the  east  coast  where  Max  was  attending  school  at  NYU.  She  

and  I  reconnected  and  began  to  discuss  the  challenges  of  such  complex  disasters,  her  work  

with  Occupy  Sandy  and  my  work  with  the  meteorological  community.  With  another  

colleague,  Katrina  Petersen,  we  started  the  DisasterCollaboratory  website,  a  hub  where  we  

envisioned  scholars  interested  in  working  with  communities  affected  by  all  kinds  of  

disasters  might  connect  with  people  on  the  ground  looking  for  resources  and  research.  

That  website  stayed  live  for  almost  three  years  before  we  all  became  too  busy  to  sustain  it,  

though  we  still  have  an  active  Facebook  page.  (My  hope  is  to  regenerate  something  similar  

post-­‐PhD).  At  the  4S  conference  in  Barcelona,  we  talked  one  afternoon  about  the  challenges  

we’ve  mutually  encountered  doing  fieldwork  in  disaster  zones  and  our  frustrations  with  

feelings  that  we’ve  come  to  recognize  as  “compromised.”    The  following  article  is  one  result  

of  that  conversation.    

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  Max  is  an  activist  scholar  and  now  an  associate  professor  at  Memorial  University  of  

Newfoundland  who  exercises  her  feminist  and  indigenous  politics  in  her  research  

(https://maxliboiron.com/).  One  commitment  for  her  is  that  in  discussing  authorship  of  

the  article,  which  we  did  before  we  began  to  write,  she  argued  that  she  wanted  lead  

authorship  to  go  to  the  scholar  who  needed  the  credit  most,  thus  “exercising  our  politics  in  

our  publications.”  As  a  junior  scholar,  we  decided  I  would  take  the  lead  even  though  she  

spent  more  time  helping  me  outline  and  organize  the  article  toward  our  publication  venue,  

an  edited  collection  “The  New  Environmental  Crisis:  Hazard,  Disaster,  and  the  Challenges  

Ahead”  (editors  James  Kendra,  Scott  Knowles,  and  Tricia  Wachtendorf  published  by  

Springer).  While  we  each  generated  fifty  percent  of  the  work,  Max  took  the  lead  in  some  of  

our  conversations  over  Skype  about  how  to  structure  the  article,  how  to  frame  our  cases,  

and  the  kinds  of  conclusions  we  might  make.      

  We  chose  this  collection  for  two  reasons:  First,  Max  and  I  know  and  admire  Scott  

Knowles,  historian  of  technology  and  author  of  Disaster  Experts.  Together,  we  are  also  part  

of  a  newer  sub-­‐group  of  scholars  that  arose  after  Katrina  but  found  stronger  coherence  

after  Fukishima;  we  are  focused  on  theorizing  Disaster  STS  (D-­‐STS),  a  sub-­‐field  that  will  be  

detailed  in  the  new  STS  Handbook.  We  have  worked  together  to  create  a  D-­‐STS  website  

(http://disaster-­‐sts-­‐network.org/)  with  resources  and  have  put  together  sessions  on  D-­‐STS  

at  various  conferences,  including  4S,  SHOT,  and  AAA.  To  bring  this  community  more  

centrally  into  my  work  with  the  meteorological  community,  I  invited  Kim  Fortun,  Scott  

Knowles,  Vivian  Choi,  and  Max  to  a  workshop  in  Norman  Oklahoma  last  May  called  “Living  

With  Extreme  Weather”  (http://extremeweather.ou.edu/).  I  sat  on  the  planning  committee  

for  this  group  and  have  as  a  goal  infusing  STS  with  more  problems  identified  from  within  

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my  research  domain  and  bringing  into  weather  hazards  more  STS  scholarship.  Our  

manuscript  has  been  accepted  by  the  editor  and  has  been  revised  based  on  a  cursory  

internal  review.  

  The  second  reason  we  chose  the  edited  collection  is  its  stated  goals,  which  closely  

align  with  our  own—or  at  least  offer  the  opportunity  for  our  D-­‐STS  goals  to  be  visible.  

From  their  call  for  proposals  (https://sites.udel.edu/drc/files/2015/04/DRC-­‐Call-­‐for-­‐

Chapters-­‐1-­‐qjqfzr.pdf):  

How  often  do  we  come  to  the  end  of  an  article  [on  hazards  and  disaster  topics]  and  see  statements  such  as:    

• We  need  more  community  involvement  in  decisions.    • Culture  and  context  need  to  be  considered.    • We  need  diversity  in  our  research  and  initiatives.    • Problems  demand  holistic,  interdisciplinary,  and  socio-­‐technical  solutions.  • Political  will  is  necessary  and  people  need  to  move  out  of  dangerous  places.  • More  models  will  help  us  and  we  need  to  fine-­‐tune  the  message.    

    Too  often,  research  ends  with  conclusions  like  these  that  either  have  had  little  success  in  implementation,  or  do  not  stimulate  the  transformative  discourse  that  is  necessary  for  us  to  do  more  in  our  field.  We  ask  authors  to  push  beyond  these  conclusions.  We  construe  this  book  not  as  a  standard  literature  review  or  a  path  to  the  same  conclusions  we’ve  heard  before.  Rather,  we  see  this  as  a  source  of  guidance  for  future  research,  an  assessment  of  present  knowledge  that  can  be  useful  to  policymakers,  a  goad  to  action  both  in  research  and  policy  for  students  as  well  as  those  more  established  in  the  field.  We  are  looking  for  a  collection  of  essays,  bold  in  their  approach,  that  will  differ  somewhat  in  style  and  content  from  that  expected  in  an  anthology.  We  are  looking  for  essays  that  will  be  theoretically  rigorous  but  that  will  take  surprising  perspectives  or  tilt  thinking  in  new  directions.  The  aim  is  to  be  provocative  and  unexpected.  

 

  We  wrote  the  following  article,  then,  to  connect  our  work  to  the  D-­‐STS  community  

and  to  offer  a  new  way  to  think  about  disaster  work  conducted  on  the  ground  in  light  of  

ethical  dilemmas  we  have  both  faced.  We  also  aim  to  be  bold,  perhaps  provocative,  in  our  

approach,  style,  and  content—thus  the  use  of  graphics  to  illustrate  different  tactics  we  

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might  take  in  our  situated  compromise.  Our  claim  is  that  the  context  of  disasters  heightens  

the  need  for  researchers  to  consider  first  the  ethical  dimensions  of  their  work  and  the  

methods  they  select.  Our  opinion  is  that  most  research  follows  a  process  that  is  more  

oriented  around  the  problem  definition  and  methodological  considerations  first  with  

ethical  implications  for  the  researcher  and  researched  following  from  this.  This  is  not  true  

of  every  researcher  or  every  project,  of  course,  but  as  our  article  explains,  we  both  found  

ourselves  revisiting  ethics  after  the  problem  and  method  had  been  engaged.  We  both  felt  

that  our  respective  experiences  demonstrated  that  what  we’ve  called  the  “high  stakes”  

outcomes  of  disaster  research—namely  potential  fatalities  and  harm—creates  a  particular  

kind  of  compromise  for  the  disaster  researcher.  In  many  ways,  it  resembles  concerns  raised  

by  those  who  do  community  based  participatory  action  research,  as  well  as  those  who  offer  

criticisms  of  the  IRB  as  a  mechanism  that  is  at  once  valuable  and  potentially  dangerous.  In  

the  end,  such  compromise  generates  a  particular  onus  on  the  disaster  research  to  consider  

ethics  of  justice  and  relationality  first  and  let  methods  and  problem  definition  follow.    

  Part  of  the  challenge  in  including  a  co-­‐authored  piece  in  my  dissertation  is  that  it’s  

difficult  for  me  to  make  wholesale  or  larger  changes  to  the  manuscript  based  on  committee  

feedback.  I  can  adjust  my  case  more  easily  and  address  language  or  concepts  that  need  

nuanced  revision.  But  rewriting  the  article  toward  different  aims  or  renegotiating  Max’s  

work  is  much  more  difficult.  This  article  represents  a  synthesis  of  our  joint  epistemologies  

and  ontologies—neither  wholly  one  of  us  or  the  other.  Still,  I  think  the  value  of  

demonstrating  in  my  dissertation  both  a  collaborative  effort  with  the  D-­‐STS  community  

and  my  own  ethics  of  relationality  in  context  of  the  larger  themes  of  my  dissertation  make  

the  challenges  worth  negotiating.  To  this  end,  I’ve  contacted  Max  and  she  knows  and  has  

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approved  its  inclusion  in  the  dissertation  and  any  changes  to  my  own  case.  Other  changes  

we’ll  negotiate  together  and  with  the  editor,  though  the  timeline  for  this  revision  falls  

outside  the  timeline  for  my  defense.  Thus,  I  am  including  a  version  with  track-­‐changes  here  

that  I’ll  share  with  Max  around  my  defense  and  then  move  forward  based  on  her  comments  

and  the  editors.’  In  many  ways,  this  strikes  me  as  a  common  negotiation  of  critique  and  

feedback  from  different  reviewers.  

   

   

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Compromise  and  Action:  Tactics  for  Doing  Ethical  Research  in  Disaster  Zones  

Jen  Henderson  and  Max  Liboiron  

 

  This  collection,  New  Environmental  Crisis,  seeks  to  go  beyond  the  usual  

recommendations  that  follow  from  disaster  research,  a  call  that  mirrors  a  wider  trend  in  

academic  disciplines,  including  science  and  technology  studies  (STS),  for  action-­‐oriented  

research.  Variously  called  making  and  doing,278  an  engaged  program,279  or  a  

reconstructivist  agenda,280  the  goal  of  action-­‐oriented  research  in  STS  is  to  “improve  the  

effectiveness  and  influence  of  [...]  scholarship  beyond  the  field  and/or  to  expand  the  modes  

of  [scholarly]  knowledge  production.”281  STS  disaster  research  is  particularly  well  suited  to  

this  task  because  it  attends  to  the  externalizations  of  socio-­‐technical  systems  that  result  in  

high-­‐stakes  situations  where  we  can  potentially  intervene  to  reduce  harm  and  body  counts.  

Even  outside  of  STS,  most  disaster  research  looks  to  create  action  that  affects  material  

change  on  the  ground,  whether  through  triage,  policy  change,  transformations  to  

infrastructure  or  management  practices,  or  collaboration  with  communities.    

  Despite  a  cross-­‐disciplinary  push  for  what  we  collectively  call  action-­‐oriented  

research—a  collection  of  practices  that  aim  to  move  material  conditions  from  an  “is”  

towards  an  “ought”—we  argue  that  traditional  research  ethics  and  methodologies  do  not  

help  us  navigate  the  contradictory  positions  we  often  find  ourselves  in  when  doing  such  

work.  On  the  one  hand,  as  disaster  researchers  we  aim  to  account  for  modes  of  expertise,   278  Downey  and  Zuiderent-­‐Jerak,  “Making  and  Doing:  Engagement  and  Reflexive  Learning  in  STS.”  279  Sismondo,  “Science  and  Technology  Studies  and  an  Engaged  Program.”  280  Woodhouse  et  al.,  “Science  Studies  and  Activism:  Possibilities  and  Problems  for  Reconstructivist  Agendas.”  281  4S,  “STS  Making  and  Doing.”  

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representation,  and  political  economy  that  are  often  discounted  and  disavowed  in  disaster  

zones.282  On  the  other,  if  we  are  trying  to  effect  changes  in  material  conditions  on  the  

ground,  we  are  necessarily  using  the  very  modes  of  expertise,  representation,  and  political  

economy  we  criticize.  As  academics,  we  might  well  be  able  to  provide  a  dulcet  cultural  

critique  of  the  power  relations  inherent  in  top-­‐down  disaster  relief,  in  the  construction  of  

risk  assessments,  or  in  the  assumptions  of  expert  disaster  communications.    As  action-­‐

oriented  researchers  in  the  field  who  want  the  people  around  us  to  be  warm,  safe,  and  

healthy,  we  also  need  to  engage  with  top-­‐down  disaster  relief  agencies,  use  risk  

assessments,  and  listen  to  and  convey  expert  disaster  communications.  That  is,  we  work  

within  systems  we  have  already  deemed  deeply  problematic,  or  what  activist-­‐

anthropologist  Charles  Hale  calls  “compromised.”283    

  Hale  argues  that  this  contradictory  position  has  positive  effects  for  researchers:  as  

action-­‐oriented  researchers  we  are  “inevitably  drawn  into  the  compromised  conditions  of  

the  political  process.  The  resulting  contradictions  make  the  research  more  difficult  to  carry  

out,  but  they  also  generate  insight  that  otherwise  would  be  impossible  to  achieve.”284  For  

example,  when  canvassing  New  York  City  residents  about  their  needs  in  the  immediate  

aftermath  of  Superstorm  Sandy,  community-­‐based  organizations  found  that  data  was  

patchy  and  so  would  normally  be  thrown  out  if  traditional  sampling  and  data-­‐cleaning  

techniques  were  followed.  Yet,  using  that  same  un-­‐sampled  and  un-­‐cleaned  patchy  data  

painted  a  very  different  picture  of  the  storm  for  residents  residing  in  the  disaster  zone  

282  Fortun  et  al.,  “Disaster  STS.”  283  Hale,  “Activist  Research  v.  Cultural  Critique:  Indigenous  Land  Rights  and  the  Contradictions  of  Politically  Engaged  Anthropology.”  284  Ibid.,  98.  

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compared  to  official  accounts  that  “cleaned  up”  the  data.285  This  insight  lead  to  academic  

and  policy  papers  that  differed  from  official  accounts.286  Even  as  we  are  aware  of  these  

contradictions,  tensions,  and  compromises,  the  question  remains:  how  can  we  make  

decisions  that  lead  towards  what  we  as  researchers  see  as  positive  action  from  within  the  

“compromised  conditions  of  the  political  process”  we  are  seeking  to  change?  How,  in  short,  

do  we  do  action  while  compromised?  And  how  do  we  do  so  in  a  way  that  is  driven  by  

research  ethics  that  take  into  account  the  unexpected  situations  and  high  stakes  so  

common  in  disaster  zones?  

  Most  social  science  research  about  disasters  is  conducted  based  on  an  assessment  of  

the  problem  to  be  addressed  (literature  reviews  and  statements  of  problem)  followed  by  a  

deployment  of  methods  appropriate  to  the  research  question  (field  or  archival  research).  

Interviews,  for  example,  often  come  with  Institutional  Review  Board  (IRB)  requirements  

for  consent  forms  and  to  anonymize  data.  Ethics  follow  from  methods.  This  is  not  to  

suggest  that  ethics  and  methods  are  not  mutually  imbricated.  But  in  traditional  research  

design,  researchers  often  select  methods  based  on  the  type  of  problem  inquiry  rather  than  

the  ethical  commitments  they  themselves  have  with  the  groups  they’re  investigating.  We  

argue  that  in  action-­‐oriented  disaster  research,  it  is  the  researcher’s  ethical  commitments  

that  should  shape  and  refine  methodological  strategies  and  decisions.  Ethics  ought  to  drive  

methods.    

285  Liboiron,  “Disaster  Data,  Data  Activism:  Grassroots  Responses  to  Representations  of  Superstorm  Sandy,”  2015.  286  Alliance  for  a  Just  Rebuilding,  ALIGN,  Urban  Justice  Center,  Community  Voices  Heard,  Faith  in  New  York,  Families  United  for  Racial  and  Economic  Equality,  Good  Old  Lower  East  Side,  Red  Hook  Initiative,  and  New  York  Communities  for  Change,  “Weathering  the  Storm:  Rebuilding  a  More  Resilient  New  York  City  Housing  Authority  Post-­‐Sandy”;  Superstorm  Research  Lab,  “A  Tale  of  Two  Sandys.”  

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  This  chapter  starts  with  two  case  studies  of  “compromise”  during  disaster  research,  

and  then  provides  tactics  for  making  decisions  in  the  context  of  intractable  problems  

within  compromise  using  ethics  as  a  guiding  principle.  The  two  case  studies  are  drawn  

from  our  own  experiences  as  disaster  scholars  and  reflect  two  kinds  of  research:  fast  or  

disaster-­‐in-­‐progress  research  and  slow  or  post-­‐disaster  research.    We  use  them  to  begin  a  

pragmatic  discussion  about  how  to  be  as  ethical  as  possible  as  a  generator  and  holder  of  

knowledge—a  researcher—when  institutional  and  employment  affiliations,  IRBs,  

nondisclosure  agreements,  intellectual  property  agreements,  pressures  to  publish  or  

perish,  disclosure  requirements  to  research  offices  and  funders,  and  other  binding  

frameworks  might  imperil,  under-­‐serve,  or  replicate  unjust  power  dynamics  for  people  in  

disaster  zones.    

  In  disaster  zones,  triage  is  immanent,  not  just  in  terms  of  the  actions  one  might  feel  

obligated  to  take  in  the  immediate  aftermath  of  a  disaster,  such  as  helping  people  find  

loved  ones,  or  cleaning  up  homes.  It  also  applies  in  the  ways  such  a  term  can  reframe  how  a  

researcher  prioritizes  what  she  studies.  Triage,  then,  is  an  apt  metaphor  for  compromise,  a  

way  of  making  decisions  that  involves  an  evaluation  of  priorities  in  the  moment.  It  is  highly  

context  dependent  and  involves  responding  to  immediate  needs  as  they  arise,  while  at  the  

same  time  acknowledging  that  things  are  going  to  go  wrong,  or  already  have.  And  it  is  a  

negotiation  between  less  than  ideal  choices,  guided  by  an  overarching  ethic.      

  One  of  our  goals  in  this  article  is  to  articulate  what  ethics  during  research-­‐triage,  

and  thus  compromise,  might  look  like.  This  first  case  study  illustrates  how  the  institutional  

body  that  governs  disaster  research,  the  IRB,  required  one  of  us  to  develop  a  document  to  

clearly  define  the  scope  and  stakes  for  research  prior  to  arrival  at  a  field  site—a  U.S.  

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National  Weather  Service  forecast  office.  That  is,  it  required  anticipatory  ethics.287  Yet  this  

researcher’s  experience  in  this  particular  disaster  zone  reveals  a  need  for  a  more  emergent  

ethic  that  might  permit  a  more  complete  story  of  what  actually  occurred  to  be  told  rather  

than  a  story  reflection  the  one  researchers  and  the  IRB  predicted  it  might.  

 

Ethics  of  Relationality:  The  Inadvertent  Censure  of  Knowledge  in  Weather  Disasters  

  As  a  disaster  scholar,  I  work  with  expert  forecasting  communities  who  warn  their  

publics  about  dangerous  weather  by  creating  alerts,  called  warnings,  that  are  transmitted  

through  broadcast  media,  websites,  cell  phones,  and  social  media,  among  other  

mechanisms.  I’m  especially  interested  in  the  sociotechnical  challenges  these  experts  face  

when  issuing  warnings  for  high  risk,  high  uncertainty  weather,  such  as  flash  floods  and  

tornadoes.  Rather  than  offer  critique  from  outside  the  institutions,  however,  I  have  spent  

fourteen  months  in  three  National  Weather  Service  forecast  offices  observing  and  

interviewing  meteorologists  and  their  stakeholders  with  regards  to  their  weather  warning  

practices.  By  “studying  up”288  at  key  sites  of  power  in  the  weather  prediction  community,  I  

am  able  to  identify  issues  of  urgent  concern  that  have  material  consequences  for  those  in  

harm’s  way.  Some  action-­‐oriented  researchers  have  called  for  “studying  up”  systems  of  

power  rather  than  working  only  with  those  most  affected  by  such  institutions  because  it  is  

an  ideal  place  to  create  change  in  larger  systems.289    

287  Elwood,  “Negotiating  Knowledge  Production:  The  Everyday  Inclusions,  Exclusions,  and  Contradictions  of  Participatory  GIS  Research.”  288  Gusterson,  “Studying  Up  Revisited.”  289  Nadar,  “Up  the  Anthropologist:  Perspectives  Gained  from  Studying  up”;  Nygreen,  “Reproducing  or  Challenging  Power  in  the  Questions  We  Ask  and  the  Methods  We  Use:  A  Framework  for  Activist  Research  in  Urban  Education.”  

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  In  this  effort,  I  join  scholars  across  the  disaster  research  community  who  suggest  

the  warning  process  itself  is  beleaguered  by  multiple  and  complex  challenges.  Many  in  the  

weather  disaster  community  recognize  we  have  insufficient  knowledge  about  vulnerable  

populations  and  their  lack  of  capacity  to  access  resources  or  information,290  as  well  as  

omissions  in  addressing  their  unique  needs  in  the  context  of  warning  technologies.291  

Others  have  foregrounded  problems  in  conveying  uncertainties  of  hazard  information292  or  

definitional  issues  that  complicate  warning  success.293    Moreover,  there  is  still  a  great  need  

to  challenge  how  these  weather  “events”  are  framed  by  their  atmospheric  occurrence  (e.g.  

tornado)  and,  as  such,  lack  an  accounting  of  the  sociopolitical  underpinnings  that  shape  

material  conditions  in  the  communities  in  which  they  occur  (e.g.  poverty).294  A  valuable  site  

of  intervention  for  disaster  researchers,  then,  is  a  bureaucratic  system  where  warning  

practices  and  policies  originate  and  are  largely  taken  for  granted.  If  transformed  toward  a  

fuller  measure  of  equity  and  equality,  such  interventions  may  have  the  potential  to  effect  

systemic  change.  Yet,  even  if  we  are  in  the  right  place  to  effect  change,  in  the  actual  practice  

of  action-­‐oriented  disaster  research,  the  unexpected  can  call  into  question  strategies  for  

conducting  ethical  research  in  these  disaster  zone  communities.    

290  Anderson  et  al.,  “Far  Far  Away  in  Far  Rockaway:  Responses  to  Risks  and  Impacts  during  Hurricane  Sandy  through  First-­‐  Person  Social  Media  Narratives”;  Gall,  Nguyen,  and  Cutter,  “Integrated  Research  on  Disaster  Risk:  Is  It  Really  Integrated”;  Lazrus  et  al.,  “Vulnerability  beyond  Stereotypes:  Context  and  Agency  in  Hurricane  Risk  Communication”;  Phillips,  “Crowdsourcing  Gender  Equity.”  291  Wood  and  Weisman,  “A  Hole  in  the  Weather  Warning  System.”  292  Morss,  Lazo,  and  Demuth,  “Examining  the  Use  of  Weather  Forecasts  in  Decision  Scenarios:  Results  from  a  US  Survey  with  Implications  for  Uncertainty  Communication.”  293  Barnes  et  al.,  “False  Alarms  and  Close  Calls:  A  Conceptual  Model  of  Warning  Accuracy.”  294  Fothergill  and  Peek,  “Poverty  and  Disasters  in  the  United  States:  A  Review  of  Recent  Sociological  Findings”;  Knowles,  The  Disaster  Experts:  Mastering  Risk  in  Modern  America.  

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  During  my  fieldwork,  for  example,  I  observed  an  incident  in  which  forecasters  

missed  issuing  a  timely  tornado  warning  for  a  storm  that  struck  a  small  Western  

community,  destroying  several  homes.  The  storm  took  the  forecasters  by  surprise.    While  a  

warning  did  go  out  to  the  community  minutes  later,  this  delay  in  detecting  the  threat  went  

largely  unacknowledged  and  unexamined  in  official  accounts.  Instead,  statistical  measures  

the  NWS  uses  to  quantify  the  success  of  a  warning  reconstruct  the  event  in  ways  that  mask  

narrations  of  experience  and  complexities  of  power  relations  at  work  between  experts  and  

publics.295    I  know  about  the  full  extent  of  this  instance  because  my  relationship  with  the  

meteorologists  in  the  office,  one  based  on  mutual  trust,  created  an  opportunity  for  me  walk  

the  damage  path  with  them  the  next  day  as  they  recorded  the  severity  of  the  tornado  and  

collected  descriptions  of  the  tornado  from  those  affected  by  the  storm.  Compromise  arose  

in  that  context.    

  Because  the  IRB  approved  forms  I  had  completed  nearly  a  year  before  my  fieldwork  

only  focused  on  forecaster  practices,  the  university  IRB  representative  I  spoke  to  about  my  

dilemma  warned,  “Since  your  IRB  didn’t  cover  individuals  in  the  community  who  were  

affected  by  this  storm,  you  shouldn’t  even  transcribe  any  portion  of  the  audio  taken  from  

that  day.”  Instead,  I  should  have  created  a  more  robust  IRB  protocol  that  entailed  as  many  

possible  permutations  of  research  participants  as  I  could  imagine.  The  limitations  inherent  

in  my  own  vision  of  what  disaster  research  might  entail,  then,  precluded  me  from  building  

a  protocol  that  might  be  as  flexible  and  messy  as  the  disasters  I  might  encounter.  Yet,  those  

of  us  who  conduct  such  work  know  that  disasters  occur  when  normal  modes  of  life  are  

suspended.  As  such,  surprises  and  unexpected  issues  are  inevitable,  which  necessitates  that  

295  Porter,  Trust  in  Numbers:  The  Pursuit  of  Objectivity  in  Science  and  Public  Life.  

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the  disaster  researcher  be  flexible  and  open  to  situations  as  they  arise.  Yet  institutional  

research  ethics  are  anticipatory  in  how  they  assume  risks,  norms  of  consent,  and  notions  of  

benefit  and  harm  into  our  protocols  before  we  conduct  research.  What  we  anticipate,  

however,  is  always  exceeded  by  what  is  on  the  ground.  

  The  IRB  administrator  went  on  to  explain  a  few  choices  I  had  at  that  point:    “You  can  

track  down  participants  and  get  their  permission  retroactively,”  she  said,  a  task  that  I  

explained  was  made  impossible  by  their  anonymity  during  our  initial  conversations.  “Well,”  

she  said,  “you  can  submit  a  revised  protocol  to  the  IRB  director  and  ask  that  you  be  allowed  

to  make  generalizations  about  the  community  response  without  citing  individuals.”  In  all  

likelihood,  she  explained,  I  would  only  be  allowed  to  distill  this  particular  public’s  

experiences  to  a  few  sentences.  I  would  lose  the  particularities,  the  richness,  and  the  

nuance  of  what  individuals  shared  and  how  this  might  reveal  moments  to  open  up  possible  

conversations  between  forecasters  and  their  publics.  While  I  value  the  purpose  of  the  IRB  

in  minimizing  harm  to  participants,  I  wonder  what  kind  of  harm  the  omission  of  these  

voices  might  create  for  individuals  in  the  future  should  the  publics’  experience  remain  

hidden  from  view.  My  narrative  of  this  disaster  would  remain  one-­‐sided,  limited,  

inequitable.      

  And  while  other  examples  of  problematic  warnings  of  this  same  sort  certainly  exist,  

which  might  be  used  to  illuminate  issues  between  forecast  offices  and  their  publics,  how  

many  of  these  were  fully  witnessed  by  a  researcher  who  could  write  about  both  sides?  How  

many  researchers  have  examined  the  lifecycle  of  a  warning  from  its  generation  to  its  affect  

on  the  community  to  its  instantiation  as  a  government  metric?  Many  such  qualitative  or  

subjective  accountings  around  sites  of  controversy  get  erased  in  the  very  classification  

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systems  that  organize  them.296  “Next  time,”  the  IRB  administrator  advised,  “I  would  plan  

for  these  kinds  of  issues  in  the  original  paperwork.”  It  is  a  lesson  I  take  forward  should  this  

scenario  repeat  itself  again,  but  what  of  other  scenarios  I  am  unable  to  foresee?    More  

importantly,  I  have  come  to  realize  that  although  IRBs  can  be  consulted  after  the  fact  to  

seek  permission  for  changes  or  unexpected  developments,  they  are  not  nimble  or  time-­‐

sensitive  instruments.  

  In  this  instance,  I  found  myself  in  a  compromised  system,  where  I  owed  my  

presence  in  the  disaster  zone  to  the  Institutional  Review  Board.  But  the  rules  set  out  by  the  

IRB  meant  that  I  couldn’t  act  the  way  I  thought  was  right.  So  what  else  can  I  do?  

296  Bowker  and  Star,  Sorting  Things  out:  Classification  and  Its  Consequences;  Porter,  Trust  in  Numbers:  The  Pursuit  of  Objectivity  in  Science  and  Public  Life.  

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  As  the  flow  chart  above  illustrates,  in  a  disaster-­‐in-­‐progress,  ethical  dilemmas  can  

arise  from  a  choice  of  methods  that  may  leave  the  researcher  in  the  intractable  position  of  

being  beholden  to  multiple  stakeholder  groups  with  mutually  exclusive  expectations  of  

conduct.  For  example,  in  terms  of  reporting  the  full  breadth  of  my  knowledge,  I  can  remain  

silent  but  in  violation  of  deeper  ethical  responsibility  to  communities  in  disaster,  or  find  

loopholes  or  create  strategies  to  reveal  what  has  not  been  approved  by  traditional  ethic  

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boards.  These  loopholes  may  cause  me  to  lose  or  imperil  my  job,  potentially  have  to  

abandon  hard-­‐won  access  to  field  sites,  or  lose  the  trust  of  my  research  participants.    

  What  the  chart  cannot  represent  is  the  internal  turmoil  I  felt  as  I  entered  the  field  

and  experienced  the  unexpected.  Knowing  that  my  research  could  unintentionally  make  

complexities  and  injustices  that  surround  tornado  disasters  invisible  has  made  me  question  

my  ability  to  transform  those  bureaucratic  systems  within  which  I’ve  worked  so  hard  to  

build  networks  of  trust.  In  disaster  zones,  relational  ethics  suggests  that  we  build  

reciprocal  relationships  “that  are  attentive  to  the  social  context  of  the  research,  the  

researcher’s  situatedness  with  respect  to  that  context,  and  the  responsibilities  which  

researchers  and  research  participants  have  toward  each  other.”297  That  is,  I  felt  as  though  I  

had  split  loyalties  between  honoring  the  ethics  of  the  IRB,  which  limited  my  ability  to  tell  

the  side  of  the  people  affected  by  the  tornado;  my  ethic  of  relationality  with  forecasters  

guided  by  my  critical  participation298  with  them  over  several  years;  and  individual  or  

community  demands  for  a  promise  of  institutional  accountability  for  public  safety.  

  These  personal  consequences  in  no  way  reflect  the  scale  of  consequences  

communities  face  in  environmental  disasters.  In  this  disaster-­‐in-­‐progress  no  one  died,  but  

they  could  have.  In  the  future,  they  likely  will.  It  is  this  fact  that  drives  me  to  operate  within  

the  expert  weather  and  climate  communities  where  structure  might  be  systemically  

transformed,  but  that  simultaneously  put  me  in  dilemmas  based  on  incommensurable  

assumptions  between  my  own  ethics  and  the  IRB.  Other  research  communities  share  this  

297  Brun,  “A  Geographers’  Imperative?  Research  and  Action  in  the  Aftermath  of  Disaster,”  204.  298  Downey,  “What  Is  Engineering  Studies  For?  Dominant  Practices  and  Scalable  Scholarship,”  2009.  

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same  feeling  of  compromise  and  have  begun  to  mount  a  critique  of  the  IRB  to  build  

flexibility  and  adaptability  into  the  system.    

  As  many  in  who  conduct  Community  Based  Participatory  Action  Research  (CBPR)  

have  argued,  current  IRB  mechanisms  are  inflexible,  in  part,  because  they  are  based  on  a  

medical  conception  of  what  it  means  to  be  human.299  They  assume  a  particular  kind  of  

asymmetry  in  power  relations  between  researcher  and  participant,300  focus  on  individual  

protection  rather  than  community  rights,301  and  are  often  unfamiliar  with  the  principles  of  

CBPR  research  in  ways  that  violate  beneficence  and  justice  with  regard  to  researcher-­‐

participant  relationships.302  Additionally,  the  promise  of  work  conducted  ethically  as  

adjudicated  by  IRB  mechanisms  may  act  as  an  unintentional  barrier  to  holding  institutions  

accountable  for  their  actions.  Compromise,  then,  may  be  an  outcome  of  competing  

promises  made  across  different  scales  and  with  varying  institutional  norms  of  

responsibility.  I  suggest  that  like  CPBR,  disaster  research  likewise  requires  an  ethics  that  

can  handle  emergent  cases,  especially  those  with  ongoing  deep  relationships  between  

participants  and  the  researchers.  Instead  of  university  IRB  officials  requiring  detailed  

protocols  articulated  in  pre-­‐established  forms  and  completed  before  fieldwork,  for  

example,  researchers  might  be  allowed  to  develop  with  their  communities  alternative  

ethics  reviews  processes  and  forms  of  consent  that  are  more  in  line  with  relationships  built  

during  and  after  crisis.  This  kind  of  local  community  review  might  be  created  in  parallel  

299  Stark,  Behind  Closed  Doors:  IRBS  and  the  Making  of  Ethical  Research;  Schrag,  Ethical  Imperialism:  Institutional  Review  Boards  and  the  Social  Sciences,  1965-­‐2009.  300  Boser,  “Power,  Ethics,  and  the  IRB.”  301  Shore  et  al.,  “Relationships  Between  Community-­‐Based  Processes  for  Research  Ethics  Review  and  Institution-­‐Based  IRBs:  A  National  Study.”  302  Brown  et  al.,  “Institutional  Review  Board  Challenges  Related  to  Community-­‐Based  Participatory  Research  on  Human  Exposure  to  Environmental  Toxins:  A  Case  Study.”  

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with  or,  in  some  cases,  in  place  of  IRB  oversight.  This  would  allow  the  researcher  and  the  

community  to  negotiate  responsibilities,  outcomes,  and  flexible  practices  not  currently  

available  in  IRB  protocols.  If  this  emergent  nature  of  disaster  research  is  not  better  

reflected  in  our  institutionalized  ethics,  it  will  continue  to  leave  the  researcher—and  their  

communities—compromised.  That  is,  will  my  compromised  position  as  a  disaster  

researcher  and  my  initial  choice  of  methods  harm  the  communities  I  care  about?  

 

Toxic  Exposures:  Community  Consultation  for  Cases  of  Unknown  Harm  

  In  the  example  above,  withholding  knowledge  was  a  primary  ethical  issue.  In  the  

example  that  follows,  sharing  knowledge  is  the  problem.  I  research  marine  plastic  pollution  

in  Newfoundland  in  northeastern  Canada.  Plastics  attract  toxic  substances  and  can  absorb  

up  to  a  million  times  more  of  a  chemical  than  in  surrounding  water;303  if  you’ve  ever  had  

curry  or  spaghetti  and  put  your  leftovers  in  plastic  tupperware,  the  difficulty  scrubbing  the  

orange  colour  out  of  the  plastic  is  a  manifestation  of  this  material  tendency  to  absorb  oily  

chemicals.  In  the  ocean,  when  these  plastics  and  their  absorbed  chemicals  are  ingested  by  

fish,  accumulated  industrial  chemicals  move  into  fish’s  bodies.304  Most  of  these  chemicals  

are  endocrine  disruptors,  which  have  been  correlated  with  infertility,  recurrent  

miscarriages,  feminization  of  male  fetuses,  early-­‐onset  puberty,  early-­‐onset  menopause,  

obesity,  diabetes,  reduced  brain  development,  cancer,  and  neurological  disorders  such  as  

303  Mato  et  al.,  “Plastic  Resin  Pellets  as  a  Transport  Medium  for  Toxic  Chemicals  in  the  Marine  Environment.”  304  Colabuono,  Taniguchi,  and  Montone,  “Polychlorinated  Biphenyls  and  Organochlorine  Pesticides  in  Plastics  Ingested  by  Seabirds.”;  Rochman  et  al.,  “Ingested  Plastic  Transfers  Hazardous  Chemicals  to  Fish  and  Induces  Hepatic  Stress”;  Tanaka  et  al.,  “Accumulation  of  Plastic-­‐Derived  Chemicals  in  Tissues  of  Seabirds  Ingesting  Marine  Plastics.”  

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early-­‐onset  senility  in  adults  and  reduced  brain  development  in  children.305  Their  effects  

are  hard  to  track  because  they  are  caused  by  other  factors  as  well,  and  can  only  be  

correlated  with  exposure  in  laboratory  settings.306    

  Many  Newfoundlanders,  particularly  those  in  rural  communities,  depend  on  fish  for  

sustenance  and  livelihoods,  and  it  is  central  to  culture  throughout  the  province.  Marine  

plastics  in  food  webs  are  a  slow  disaster  produced  by  routine,  rather  than  exceptional  or  

explosive,  exposures  to  toxic  chemicals.  Rob  Nixon’s  work  on  slow  violence  describes  these  

sorts  of  disaster,  as  they  are  “neither  spectacular  nor  instantaneous,  but  rather  incremental  

and  accretive,  its  calamitous  repercussions  playing  out  across  a  range  of  temporal  

scales.”307  If  I  find  that  fish  species  used  for  food  in  Canada  are  highly  contaminated  with  

plastics  (or  their  associated  chemicals),  then  my  research  would  describe  a  slow  disaster  in  

progress,  but  it  may  also  impact  communities  beyond  the  harm  chemicals  are  doing.    

  This  has  happened  before.  Between  September  1987  and  September  1988,  breast  

milk  was  collected  from  lactating  mothers  who  lived  on  the  east  coast  of  the  Hudson  Bay  in  

the  arctic.  Unusually  high  levels  of  polychlorinated  biphenyls  (PCBs),  a  known  carcinogen  

found  in  coolants,  were  found  in  their  breast  milk,  likely  due  to  the  mother’s  diets  of  marine  

mammals  that  are  often  contaminated  with  the  chemicals.308  Journalist  Maria  Cone  

recounts  that,  “Before  the  data  could  be  analyzed,  and  before  people  in  the  villages  were  

notified,  the  discovery  leaked  to  the  press.  On  December  15,1988,  Toronto's  Globe  and  Mail  

305  Grun  and  Blumberg,  “Endocrine  Disrupters  as  Obesogens,”  8;  Halden,  “Plastics  and  Health  Risks,”  179–94;  Bergman  et  al.,  “State  of  the  Science  of  Endocrine  Disrupting  Chemicals  2012:  Summary  for  Decision-­‐Makers.”  306  Liboiron,  “Redefining  Pollution  and  Action:  The  Matter  of  Plastics”;  Langston,  Toxic  Bodies:  Hormone  Disruptors  and  the  Legacy  of  DES.  307  Nixon,  Slow  Violence  and  the  Environmentalism  of  the  Poor,  2.  308  Dewailly  et  al.,  “High  Levels  of  PCBs  in  Breast  Milk  of  Inuit  Women  from  Arctic  Quebec.”  

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published  a  front-­‐page  story,  quoting  an  Environment  Canada  official  saying  that  the  Inuit  

were  so  contaminated  that  they  may  have  to  eat  beef  and  chicken  and  give  up  whale,  seal,  

and  walrus.  The  Inuit  were  terrified  and  some  stopped  eating  their  native  foods.”309  Breast  

feeding  also  became  taboo,  which  had  long  term  effects  on  health  and  culture.310  I  do  not  

want  a  similar  incident  to  happen  in  Newfoundland  in  the  case  of  plastics.  If  there  are  

toxicants  in  food  webs  from  plastics,  I  want  communities  to  be  able  to  determine  how  the  

information  is  presented  and  circulated  rather  than  a  top-­‐down,  stigmatizing,  incautions  

approach.  

  In  addition  to  the  cultural  violence  that  withdrawing  traditional  foods  would  

entail,311  sociologist  of  disaster  Kai  Erikson  warns  of  the  tolls  of  chronic  dread  and  vigilance  

for  those  who  live  in  contaminated  landscapes  “alive  with  dangers,  a  terrain  in  which  [...]  

benevolences  of  creation  are  to  be  feared  as  sources  of  toxic  infection.”312  Likewise,  

Communicating  about  Contaminants  in  Country  Food:  The  Aboriginal  Experience  warns  

that  "[w]hether  or  not  individuals  are  exposed  to  or  actually  ingesting  injurious  levels  of  

contaminants,  the  threat  alone  leads  to  anxiety  over  risks  to  health,  loss  of  familiar  and  

staple  food,  loss  of  employment  or  activity,  loss  of  confidence  in  the  basic  food  source  and  

the  environment,  and  more  generally  a  loss  of  control  over  one's  destiny  and  well-­‐

309  Cone,  Silent  Snow:  The  Slow  Poisoning  of  the  Arctic,  114.  310  Cone,  Silent  Snow:  The  Slow  Poisoning  of  the  Arctic;  Boswell-­‐Penc,  Tainted  Milk:  Breastmilk,  Feminisms,  and  the  Politics  of  Environmental  Degradation.  311  Reinhardt,  “Spirit  Food”;  Waziyatawin  and  Yellow  Bird,  For  Indigenous  Eyes  Only:  A  Decolonization  Handbook;  Wiedman,  “Native  American  Embodiment  of  the  Chronicities  of  Modernity:  Reservation  Food,  Diabetes,  and  the  Metabolic  Syndrome  among  the  Kiowa,  Comanche,  and  Apache.”  312  Erikson,  A  New  Species  of  Trouble:  The  Human  Experience  of  Modern  Disasters,  155.  

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being.”313  I  am  faced  with  the  possibility  that  even  my  provisional  findings  may  cause  harm,  

regardless  of  my  intentions,  caveats,  and  my  overall  research  goal  of  working  towards  

environmental  justice.  This  is  not  to  say  that  publics  panic  when  they  learn  of  

contamination  in  their  food  or  bodies;  there  are  ample  findings  to  the  contrary.314  Rather,  it  

is  to  say  that  there  are  real  types  of  harm  that  research  findings  can  do,  particularly  in  

disaster  zones,  and  I  am  trying  to  figure  out  how  to  be  accountable  to  those  possibilities.    

  My  job  as  a  researcher  is  not  to  simply  record,  describe,  and  report  slow  disasters.  I  

am  a  community  member  in  a  slow  disaster.  This  intersectionality  is  not  

compartmentalized  so  my  responsibilities  in  my  role  as  a  local  citizen  versus  a  university  

researcher  are  mutually  exclusive.  I  am  always  both  at  once,  so  cannot  detachedly  report  

contamination  while  living,  working,  and  eating  in  contaminated  zones,  especially  in  a  

place  where  cod  is  so  central  to  culture,  and  has  been  a  primary  source  of  food  and  

livelihood  for  settlers  since  colonization,  and  for  Aboriginal  groups  before  and  after  

colonization.  In  Newfoundland,  cod  is  life.  The  cod  fishery  collapsed  in  the  early  1990s315  

and  the  government’s  cod  moratorium  resulted  in  the  largest  job  loss  in  Canadian  history,  

exacerbating  the  already  high  unemployment  and  poverty  rates  in  Newfoundland.316  I  have  

been  to  diners  in  outport  Newfoundland  (they  mostly  serve  cod)  where  the  newspaper  

announcing  the  moratorium  is  laminated  to  the  wall.  Cod  has  been  through  a  lot  here.  The   313  Usher,  Communicating  about  Contaminants  in  Country  Food:  The  Experience  in  Aboriginal  Communities,  113.  314  Brody  et  al.,  “Reporting  Individual  Results  for  Biomonitoring  and  Environmental  Exposures:  Lessons  Learned  from  Environmental  Communication  Case  Studies”;  Morello-­‐Frosch  et  al.,  “Communicating  Results  in  Post-­‐Belmont  Era  Biomonitoring  Studies:  Lessons  from  Genetics  and  Neuroimaging  Research.”  315  Bavington,  Managed  Annihilation:  An  Unnatural  History  of  the  Newfoundland  Cod  Collapse.  316  Schrank  and  Roy,  “The  Newfoundland  Fishery  and  Economy  Twenty  Years  after  the  Northern  Cod  Moratorium.”  

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stakes  of  telling  Newfoundlanders  of  yet  another  threat  to  cod  has  potentially  far  reaching  

effects  for  health,  culture,  and  economics.  So  what  can  I  do?  

 

 

  This  flowchart,  and  the  preceding  narrative,  make  it  seem  as  though  I  have  a  series  

of  well-­‐defined  decisions  to  make  after  thoughtful  consideration,  and  I  can  choose  between  

different  unifying  ethics  to  guide  me  through  the  research.  In  reality,  I  had  already  used  

traditional  research  methods  to  gather  cod  fish  guts  and  had  started  dissecting  them  before  

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I  realized  that  if  I  found  a  high  amount  of  contamination,  I  had  a  problem.  I  was  over  a  sink  

with  the  partially  digested  contents  of  a  cod’s  last  supper  running  through  my  fingers  when  

I  thought,  “Holy  shit.  This  might  be  bad.”  A  feeling  of  dread  and  foreboding  stole  over  me  

long  before  I  could  articulate  the  problem  in  the  way  I  have  described  above,  even  though  I  

was  already  well  acquainted  with  the  breast  milk  contamination  story  and  am  well-­‐attuned  

to  the  importance  of  traditional  foods  to  local  cultures.    

  What  happened?  I  finished  the  study.  I  found  some  of  the  lowest  plastic  ingestion  

rates  ever  recorded  (publication  forthcoming).  Before  my  students  and  I  started  writing  up  

the  findings,  I  held  a  public  meeting  hosted  in  one  of  the  fishing  communities  I  gathered  cod  

guts  from  to  discuss  the  research.  The  meeting  was  well  attended,  and  the  room  was  

palpably  tense  as  I  spoke  about  how  marine  plastics  and  contamination  worked,  and  about  

our  methodology  of  gathering  cod  guts  from  local  fish  harvesters.  The  moment  I  shared  our  

findings-­‐-­‐that  we’d  found  the  lowest  plastic  ingestion  rates  ever  recorded-­‐-­‐people’s  arms  

uncrossed,  they  started  laughing  at  my  jokes,  and  talking  out  of  turn.  Many  meeting  

attendees  celebrated  the  low  plastic  ingestion  rate  of  their  fish.  Yet  a  low  plastic  ingestion  

rate  is  not  a  harmless  rate.  The  problem  of  plastic  ingestion  is  likely  to  get  worse  as  

increasing  amounts  of  plastics  are  produced  and  flow  into  oceans.    

  My  decisions  about  publication  and  future  research  are  still  not  as  clear  as  the  chart  

above  might  indicate,  and  I  realize  there  are  a  myriad  of  options  that  I  have  not  anticipated  

and  are  not  on  the  chart.  However,  the  chart  does  provide  guidance,  and  gives  me  the  space  

to  think  about  my  options  rather  than  automatically  following  methodological  courses  of  

actions  common  to  university  research  (data  collection  >  findings  >  publish  >  repeat).  I  

intend  to  form  a  community-­‐based  advisory  committee  that  recommends  what  kind  of  

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research  questions  are  the  most  important  to  consider,  what  aspects  of  the  slow  disaster  to  

focus  on,  and  how  to  best  mobilize  and  disseminate  our  findings  (or  not).  But  even  this  

strategy  does  not  avert  compromise.  In  fact,  it  will  put  me  in  a  more  compromised  position  

if  my  university,  my  funders,  and  my  advisory  board  disagree,  which  seems  rather  

inevitable  given  different  priorities  and  values.  What  will  happen  if  my  advisory  board  

thinks  I  should  not  publish  or  disseminate  findings?  What  about  my  responsibilities  to  

people  who  eat  our  fish?  Thinking  through,  and  even  leveraging,  the  tensions  that  arise  

when  working  from  within  an  academic  institution  with  research  ethics  that  come  from  

outside  forums  is  not  new,317  but  it  is  a  key  context  through  which  to  think  about  

compromise  and  action.    

 

Living  and  Working  in  Compromise  

  Anthropologist  Charles  Hales  makes  a  sharp  distinction  between  cultural  critique,  

where  “political  alignment  is  manifested  through  the  content  of  the  knowledge  produced”  

and  activist  research  (his  term),  where  politics  happen  “through  a  relationship  established  

with  [...]  people  in  struggle.”318  The  latter  “requires  a  substantive  transformation  in  

317  Elwood,  “Negotiating  Knowledge  Production:  The  Everyday  Inclusions,  Exclusions,  and  Contradictions  of  Participatory  GIS  Research”;  Halvorsen,  “Militant  Research  Against-­‐and-­‐Beyond  Itself:  Critical  Perspectives  from  the  University  and  Occupy  London”;  Russell,  “Beyond  Activism/Academia:  Militant  Research  and  the  Radical  Climate  and  Climate  Justice  Movement”;  Russell,  Pusey,  and  Chatterton,  “What  Can  an  Assemblage  Do?  Seven  Propositions  for  a  More  Strategic  and  Politicized  Assemblage  Thinking”;  Saxton  et  al.,  “Environmental  Health  and  Justice  and  the  Right  to  Research:  Institutional  Review  Board  Denials  of  Community-­‐Based  Chemical  Biomonitoring  of  Breast  Milk”;  Schrag,  Ethical  Imperialism:  Institutional  Review  Boards  and  the  Social  Sciences,  1965-­‐2009;  Taylor,  “‘Being  Useful’  after  the  Ivory  Tower:  Combining  Research  and  Activism.”  318  “Activist  Research  v.  Cultural  Critique:  Indigenous  Land  Rights  and  the  Contradictions  of  Politically  Engaged  Anthropology,”  98.  

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conventional  research  methods  to  achieve  these  goals.”319  Action-­‐oriented  research  has  a  

different  context  of  ethical  deliberation  in  which  relational  ethics—rather  than  non-­‐

anticipatory  ethics—is  important.  And  so  it  requires  different  methods  and  methodologies.  

Compromise  occurs  through  this  difference,  such  as  when  a  disaster  researcher  has  to  take  

account  of  different  commitments  to  different  parties.    

  In  both  of  our  case  studies,  researchers  came  to  an  ethical  dilemma  in  the  middle  of  

research  in  spite  of  previous  experience  in  disaster  contexts.  This  will  continue,  even  if  we  

try  to  anticipate  the  unexpected.    It  is  clear  that  a  step-­‐by-­‐step  guide  for  action-­‐based  

research  in  disaster  zones  is  impossible  because  action  is  context  dependent  and  every  

disaster  is  unique.  But  ethics  can  carry  across  contexts  and,  once  we  know  what  the  

“greatest  good”  or  highest  commitment  in  our  work  is,  it  can  guide  actions  in  a  variety  of  

situations.  We  suggest  that  Disaster  STS  can  be  a  leader  in  thinking  through  these  issues  

because  of  its  high  stakes;  while  many  research  areas  include  action-­‐oriented  research  that  

will  put  researchers  in  difficult  positions,  disaster  research  does  so  immediately  and  often,  

making  it  one  ideal  context  to  investigate  these  issues.  

  This  is  not  to  say  we  should  throw  away  IRB  ethics.  Our  research  ethics  align  with  

the  basic  principles  of  justice,  beneficence,  and  doing  no  harm;  our  methods  will  always  

entail  informed  consent  and  the  option  for  anonymity.  But  they  can  also  foreground  

alternative  ethics.  For  example,  action-­‐oriented  researcher  Ernest  Stringer  foregrounds  

pride  (people’s  feelings  of  self-­‐worth),  dignity  (people’s  feelings  of  autonomy  and  

independence),  control  (people’s  control  over  their  own  researches,  decisions,  actions,  and  

insights,  including  data),  and  responsibility  (people’s  ability  to  be  accountable  for  their  

319  Ibid.  

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own  actions)  in  his  work  using  Participatory  Action  Research  methodologies.320  

Community  psychologist  Stephen  Fawcett  calls  for  guiding  values  of  collaboration,  

experimentation,  and  sustainability,  among  others.321  Action-­‐based  research,  particularly  

in  disasters,  comes  from  a  different  context  than  the  medical  context  of  institutional  ethics  

and  so  understands  the  subjects,322  methods,323  and  goals324  of  research  differently  from  

those  within  which  the  IRB  developed.    

  We  argue  that  ethics  should  drive  choice  of  methods,  not  the  other  way  around.  If  

our  greatest  commitment  is  solidarity  with  vulnerable  populations325  or  social  

movements,326  for  example,  then  these  ethics  will  dictate  whether  and  how  we  do  

interviews  (paid  or  unpaid,  collaborative  or  top-­‐down),  surveys  (community-­‐built  and  

conducted,  or  not),  how  and  where  we  draw  our  samples  and  the  overall  the  boundaries  of  

our  research  site.327  It  will  determine  how  (and  with  whom)  we  will  make  decisions  when  

the  unexpected  occurs.  One  possibility,  for  example,  is  that  after  researchers  think  about  

their  own  ethical  commitments  and  design  their  research  accordingly,  they  have  them  

reviewed  (formally  or  informally)  by  community  groups,  since  one  way  to  tell  if  your  ethics  

are  just  for  people  in  disaster  zones  is  to  have  them  adjudicated  by  those  on  the  ground  in  a   320  Stringer,  Action  Research,  23.  321  “Some  Values  Guiding  Community  Research  and  Action.”  322  Denzin  and  Giardina,  Ethical  Futures  in  Qualitative  Research:  Decolonizing  the  Politics  of  Knowledge.  323  Schrag,  Ethical  Imperialism:  Institutional  Review  Boards  and  the  Social  Sciences,  1965-­‐2009.  324  Lewis,  “Ethics,  Activism  and  the  Anti-­‐Colonial:  Social  Movement  Research  as  Resistance.”  325  Nelson  et  al.,  “‘Nothing  about  Me,  without  Me’:  Participatory  Action  Research  with  Self-­‐Help/Mutual  Aid  Organizations  for  Psychiatric  Consumer/Survivors.”  326  Situaciones,  “On  the  Researcher-­‐Militant.”  327  For  an  example  of  how  sampling  techniques  can  be  tied  to  justice  problems  in  disaster  zones,  see  Liboiron,  “Disaster  Data,  Data  Activism:  Grassroots  Responses  to  Representations  of  Superstorm  Sandy,”  2015.  

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kind  of  ethics  peer-­‐review.  We  have  attached  a  Memorandum  of  Understanding  (See  the  

Appendix)  for  research  in  disaster  zones  developed  in  consultation  with  both  researchers  

and  disaster  survivors  in  the  aftermath  of  Hurricane  Sandy.  It  was  designed  to  address  

many  of  the  ethical  dilemmas  and  mistakes  experienced  by  both  groups,  and  draws  heavily  

on  tribal  research  ethics,  where  research  ethics  have  received  substantial  scrutiny  and  

revision  that  go  far  beyond  what  IRBs  require.  This  is  not  the  end  of  a  process  of  peer  

reviewing  ethics,  however.  It  is  ongoing.  There  are  many  other  possibilities  for  how  to  

enact  research  ethics  in  disaster.    

  In  an  important  sense,  the  process  of  research  is  a  form  of  action.  We  are  not  the  

first  to  suggest  that  data  collection  is  a  process  of  negotiation  where  our  collection  

techniques  have  effects  in  the  field.  We  can  arrange  our  methods  so  they  aim  to  make  

positive  change  at  all  points  in  the  research  process,  rather  than  only  at  the  end  once  

findings  are  achieved.328  Moreover,  it  is  not  only  action-­‐oriented  researchers  in  disaster  

zones  who  navigate  compromised  systems;  in  many  ways,  compromise  is  not  a  choice  for  

any  of  us  who  produce  and  hold  knowledge.  Even  those  who  don’t  grapple  with  ethical  

dilemmas  are  compromised  because  we  all  are  always  already  participating  in  a  system  of  

power.329  One  of  the  premises  of  STS  is  that  there  is  no  outside  of  politics  for  research,  

scientific  or  otherwise.  The  nature  of  disaster  research  makes  this  especially  visible  in  our  

own  work,  and  invites  us  to  be  accountable  to  it.    

 

 

328  Stringer,  Action  Research.  329  Rose,  “Situating  Knowledges:  Positionality,  Reflexivities  and  Other  Tactics”;  Kobayashi,  “Coloring  the  Field:  Gender,  ‘Race,’  and  the  Politics  of  Fieldwork.”  

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Acknowledgements  

Funding  for  this  research  was  provided  by:  Social  Science  and  Humanities  Research  Council  

(SSHRC)  Insight  Development  Grant  (#430-­‐2015-­‐00413);  Marine  Environmental  

Observation  Prediction  and  Response  Network  (MEOPAR);  the  Advanced  Study  Program  

Graduate  Student  Visitor  Fellowship  at  the  National  Center  for  Atmospheric  Research;  and  

the  Interdisciplinary  Graduate  Education  Program  in  Remote  Sensing  at  Virginia  Tech.      

165

Conclusion   Last  week,  I  had  the  opportunity  to  present  my  work  at  the  National  Academy  of  

Sciences  in  Washington  D.C.  for  a  report  called  Advancing  Social  and  Behavioral  Science  

Research  and  Application  within  the  Weather  Enterprise.  It  was  a  humbling  experience  on  

many  levels.  My  colleagues  invited  me  to  talk  to  them  about  the  kinds  of  research  related  to  

forecasters  that  I  felt  might  be  missing  from  current  social  science  agendas.  I  had  several  

suggestions,  of  course,  and  I  highlighted  aspects  of  forecaster  practices,  experiences,  

vulnerabilities,  and  technologies  that  we  understand  so  little  about.  More  importantly,  

however,  I  presented  my  case  in  front  of  different  audiences,  including  administrators  from  

the  National  Weather  Service  and  a  few  representative  forecasters  from  the  larger  weather  

community.    

  After  my  presentation,  a  few  members  of  the  NWS  approached  me  and  asked  about  

my  dissertation.  What  was  I  doing?  What  were  my  findings?  What  had  I  learned?    

  “I’m  looking  at  the  ethical  dimensions  of  NWS  warning  practices,”  I  said.  One  man  

raised  his  eyebrows.  “Ethics?”  I  explained  the  types  of  values  I’d  explored  in  my  work,  

accuracy  and  the  man-­‐machine  mix,  care  and  concern  during  moments  of  crisis.  But  before  

I  could  get  to  my  conclusions,  my  contribution  to  the  world,  I  could  tell  he  was  confused.  

“So  you’re  looking  at  little  “e”  not  big  “E”  ethics.  So  things  like  verification,  accuracy,  skill….”  

He  trailed  off.  From  other  conversations  of  this  sort,  I  suspected  that  an  underlying  

question  for  him  was  this:  How  will  this  help  us  be  better  forecasters?    

  For  just  a  few  minutes,  I  panicked.  Not  because  I  don’t  believe  in  what  I’m  doing  or  

feel  strongly  about  my  efforts  to  theorize  a  new  image  of  the  forecaster,  one  that  arises  

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amid  what  I  have  called  empathetic  accuracy.  I  feel  strongly  that  this  concept  is  important  

to  forecasters  on  many  levels.  It  could  help  them  re-­‐see  their  science  via  an  accuracy  

developed  through  a  deeper  engagement  with  their  publics.  It’s  a  concept  that  could  

assuage  their  anxieties  over  being  replaced  by  machines  since  this  newly  cast  accuracy  

through  care  accounts  for  more  than  what  comes  from  competing  against  computer  

models.  And  as  an  organization,  the  NWS  might  see  different  ways  to  educate  and  train  

forecasters  to  be  better  suited  in  their  new  roles  as  relationship  builders  with  partners  and  

with  their  communities.    

  Yet,  I  realized  as  I  conversed  with  this  man  that  forecasters  might  reject  my  ideas.  

They  might  find  my  critique  and  my  suggestions  unrealistic,  untenable,  or  worse,  useless.  I  

wondered,  how  can  I  convince  them  of  my  ideas  now  that  they  have  been  argued  on  paper?  

What  examples  and  analogies  might  I  draw  on?    Will  it  be  enough  to  write  shorter  

summaries  of  my  dissertation  for  their  publications?  What  will  I  present  at  their  

conferences?  These  are  questions  I  must  grapple  with  as  I  move  forward  and  continue  my  

engagement  with  this  community.  I  imagine  that  it  is  through  trial  and  error,  and  a  number  

of  lengthy  conversations  with  colleagues,  that  I’ll  be  able  to  begin  to  make  progress.  

  But  first  I  feel  I  ought  to  understand  what  some  of  the  grounds  are  for  rejecting,  or  

even  co-­‐opting,  an  ethic  of  empathetic  accuracy.  I’d  like  to  spend  just  a  little  time  thinking  

“against  the  grain”  of  my  own  work  to  generate—or  anticipate—how  it  might  “travel”  or  

fail  to  do  so.  Please  indulge  me  for  just  a  few  more  minutes:  

  1.  Empathetic  accuracy  might  fall  flat  as  too  emotional,  too  much  about  love  and  “non-­‐

scientific”  concerns.  In  short,  it  sounds  feminine.  

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  The  language  of  empathy  is  one  I’ve  advocated  for  before.  In  a  presentation  at  the  

American  Meteorological  Society  a  few  years  ago,  I  talked  about  care  as  an  important  

dimension  of  forecaster  work.  I  argued  that  their  understanding  of  partners,  their  desire  to  

know  more  about  them,  comes  from  a  place  of  love.  This  presentation,  jokingly  dubbed  “the  

love  bomb”  by  my  friends,  had  mixed  results  with  forecasters.  Some  were  moved  by  my  

argument;  others  respectfully  told  me  to  talk  about  passion,  not  love.  “We’re  passionate  

about  our  work,”  one  said.  “But  as  ISTJs”  (a  reference  to  the  most  common  Myers-­‐Briggs  

personality  type  in  the  NWS)  “we’re  not  motivated  by  feelings.”  I’ve  had  this  kind  of  

conversation  a  few  other  times.  Love,  emotions,  care,  concern—these  do  not  immediately  

resonate  with  forecasters  as  values  they,  as  scientists,  ought  to  cultivate  or  recognize.  It  

sounds  like  I’m  asking  them  to  conform  to  stereotypes  of  being  a  female,  a  gendered  way  of  

thinking  about  science  that  situates  masculine  as  emotionless  and  thus  appropriate  to  a  

rational  enterprise.  The  challenge  for  me  will  be  to  find  the  language  forecasters  already  

use,  and  examples,  cases,  and  analogies,  that  will  appeal  to  them.  I  suspect  this  work  will  

involve  discussions  about  their  labor,  their  losses,  and  the  contexts  and  outcomes  of  

weather  disasters.    

  2.  Empathetic  accuracy  might  be  seen  as  too  simple  an  image  for  the  forecaster,  one  

that  presents  them  as  “good  guys.”    

  Offering  an  image  of  the  forecaster  as  one  whose  accuracy  comes  through  caring  

might  seem  as  though  I’m  looking  at  the  profession  through  rose-­‐colored  glasses.  The  

forecasters  in  my  work  are  good,  hardworking  people,  perhaps  represented  as  more  two-­‐  

dimensional  than  three.    Where  is  the  complication,  some  might  ask?  It’s  true  that  the  

image  I  propose  is  partly  an  ideal,  but  it  also  reflects  the  realities  and  lived  experiences  of  

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forecasters  based  on  years  of  observations  and  conversations  with  them.    Empathetic  

accuracy  reveals  a  fuller  range  of  the  complexities  that  comprise  forecaster  identities.  It  is  

also  one  that  rivals  the  dominant  image,  which  I  see  as  a  much  more  simplified  and  

idealized  image  of  the  forecasting  scientist  primarily  as  predictor.  This  is  a  one-­‐

dimensional  image  in  how  it  invokes  a  largely  invisible  cadre  of  professionals  working  

behind  screens  to  get  the  forecast  and  warnings  correct  and  to  do  so  according  to  narrow  

policies  and  procedures.  An  ethic  of  empathetic  accuracy  offers  a  richer,  multifaceted  

professional  image  of  what  forecasters  already  value  and  perform.  And  it  doesn’t  subtract  

from  what  they  value  as  scientists.    

  3.  Empathetic  accuracy  might  not  be  measurable  or  bureaucratically  feasible.    

  Many  at  the  NWS  might  ask,  as  some  already  have,  how  do  we  measure  empathetic  

accuracy?  Empathetic  accuracy,  I  suggest,  is  not  amenable  to  standard  practices  of  

measurement  and  quantification.  Assessments  of  relationality  can  arise  from  

understanding  motives,  practices,  and  values,  and  through  engaging  in  reflexive  activities  

that  ask  whose  needs  are  (not)  being  met  and  why.  In  many  ways,  the  forecasting  

community  already  relies  on  qualitative  accounts  (e.g.  anecdotes)  to  support  changes  in  the  

system.  I  understand  the  bureaucratic  responsibility  to  demonstrate  success  along  certain  

metrics,  especially  since  the  agency  is  responsible  to  Congressional  oversight  and  

budgetary  demands.  I  don’t  deny  this  reality.  And  it  may  limit  how  this  concept  is  deployed  

in  forecast  offices.  Still,  I  envision  that  together,  we  might  develop  local  mechanisms  of  

assessment  that  draw  on  qualitative  evidence  and  that  represent  the  various  dimensions  of  

what  might  count  as  success.  Importantly,  I  hope  that  this  new  concept  allows  the  weather  

community  to  move  beyond,  or  at  least  complement,  statistical  metrics  that  are  already  

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shown  to  be  problematic.  And  I  hope  it  helps  trouble  tacit  metrics  that  keep  the  community  

focused  on  their  failures  as  a  measure  of  losses  in  terms  of  body  counts  and  dollar  amounts.    

  4.  Finally,  empathetic  accuracy  might  be  co-­‐opted  as  a  way  of  justifying  efforts  or  

initiatives  that  I  critique.  Or  empathetic  accuracy  might  “go  wrong”  in  practice,  negatively  

affecting  forecasters  or  their  efforts  at  protection.    

  Some  may  think  that  encouraging  forecasters  to  be  more  empathetic  could  create  

conditions  in  which  the  emotions  of  care  might  interfere  with  the  science  or  objectivity  of  

forecasters.  To  them,  I  would  say  this  care  already  exists  and  affects  decisions,  

relationships,  and  outcomes.  What  we  need  is  to  talk  openly  about  care  and  examine  how  it  

currently  functions  and  ought  to  function  at  various  scales  of  practice.  What  are  the  

variations  of  care?  Where  does  it  emerge  most  strongly?  How  can  we  cultivate  it  or  

challenge  its  limitations?  My  sense  is  that  we  can’t  continue  to  ignore  or  downplay  the  

humanity  of  forecasting,  for  this  will  have  consequences  for  the  health  of  those  who  feel  the  

strain  of  concern  that  goes  unacknowledged  and  unaddressed—as  guilt  and  sadness,  as  a  

manifestation  of  health  related  issues,  such  as  post  traumatic  stress,  and  as  it  shapes  hyper  

vigilance  or  apathy  in  future  disaster  contexts.  Care,  then,  is  an  obligation  that  ought  to  

emerge  from  the  relationship  between  the  NWS  as  an  agency  and  its  staff.  

   I  am  not  the  first  to  suggest  that  science  ought  to  be  integrated  with  empathy.  

Unbeknownst  to  me,  Thomas  Friedman  recently  put  forward  the  notion  of  STEMpathy,  or  a  

way  of  connecting  sciences  and  engineering  with  the  social  skills  drawn  from  the  

humanities.  As  one  article  that  summarized  a  speech  he  gave  on  the  subject  in  2015,  notes    

Computers  and  algorithms  can  be  trained  to  do  almost  anything,  though  they  have  no  inherent  social  and  interpersonal  skills.  Therefore,  being  able  to  work  in  STEM  

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areas  and  also  being  able  to  communicate  and  collaborate  with  other  humans  is  a  key  advantage  for  individuals  entering  an  increasingly  competitive  job  market.”330    

 

STEMpathy  is  an  economic  future-­‐making,  a  way  of  framing  a  solution  to  a  competition  

with  machines  that  has  automated  people  out  of  their  jobs,  or  displaced  them  in  other  

ways.  Is  this  what  I  mean?  Is  my  concept  about  creating  more  employable  forecasters?  In  a  

way,  yes.  I  hope  to  affect  the  education,  training,  and  work  of  the  forecaster  so  that  it  better  

matches  their  goals  of  protecting  lives.  They  need  these  skills  to  be  better  protectors  and  to  

do  so  ethically.  But  mine  is  not  a  political  economy  of  forecasting,  though  I  see  how  it  could  

be  used  to  those  ends.  Instead,  because  my  dissertation  derives  its  motivation  from  

feminist  theories,  I  am  more  concerned  with  the  creation  of  a  better  science  based  on  a  

multiplicity  of  perspectives  gained  through  meaningful  and  trusting  relationships.  It  is  the  

relationships  themselves  that  inform  forecast  and  warning  practices  and  allow  forecasters  

to  attend  to  a  variety  of  needs.  I  am  not  concerned  with  improving  employment  chances  

but  evolving  forecaster  science  through  experts’  relationships  with  their  publics.  It  is  my  

hope  that  by  continually  working  with  this  community  I  can  keep  this  concept  from  being  

used  to  other  ends—or  at  least  challenge  such  uses  when  they  occur.      

  I’m  sure  my  work  has  many  other  limitations  that  I  haven’t  yet  encountered.  But  I  

like  to  think  these  can  be  addressed  or  at  least  explored  in  meaningful  ways.  Moving  

forward  from  this  dissertation,  I’ll  continue  to  critically  participate  with  the  weather  

community  to  figure  out  ways  to  address  our  collective  concerns.  It’s  not  my  place  to  solve  

these  problems  but  to  work  with  forecasters  and  offer  insights  that  enable  new  ways  of  

seeing  a  problem’s  contours  and  boundaries—or  what  those  problems  may  hide.  This  

330    (http://www.cobrt.com/education-­‐workforce/11/4/2015-­‐thomas-­‐friedman-­‐on-­‐stem)  

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requires  a  lot  of  listening,  brainstorming,  theorizing,  and  then  acting  together  toward  that  

future.    

  I  didn’t  get  to  explain  the  concept  of  empathetic  accuracy  to  my  colleagues  at  the  

National  Academies  last  week.  We  didn’t  have  enough  time  and  I  didn’t  feel  confident  

enough  in  my  explanations  to  re-­‐engage  the  subject.  Moving  forward  from  today,  however,  

I  am  enthusiastic  about  translating  my  dissertation  into  languages,  examples,  and  

publications  that  will  resonate  with  forecasters.  And  I  hope  to  contribute  my  efforts  to  the  

larger  Disaster  STS  community  in  mobilizing  STS  insights  toward  helping  practitioners  and  

first  responders  effect  systematic  change;  and  I  hope  that  my  travels  among  forecasters  

shifts  the  Disaster  STS  community  toward  a  greater  attention  to  the  effects  of  weather,  its  

disasters,  and  its  expertise  on  society.  

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Bibliography   4S.  “STS  Making  and  Doing.”  Professional  Society  Website.  Society  for  the  Social  Studies  of  

Science,  2016.  http://www.4sonline.org/meeting/sts_making_and_doing_call_for_submissions.  

“Accuracy.”  Oxford  English  Dictionary.  Oxford,  England:  Oxford  University  Press,  2016.  Alliance  for  a  Just  Rebuilding,  ALIGN,  Urban  Justice  Center,  Community  Voices  Heard,  Faith  

in  New  York,  Families  United  for  Racial  and  Economic  Equality,  Good  Old  Lower  East  Side,  Red  Hook  Initiative,  and  New  York  Communities  for  Change.  “Weathering  the  Storm:  Rebuilding  a  More  Resilient  New  York  City  Housing  Authority  Post-­‐Sandy.”  Nonprofit.  New  York  City,  March  2014.  http://www.cvhaction.org/sites/default/files/Weathering_The_Storm_Full_Report.pdf.  

American  Meteorological  Society.  “State  of  the  Weather  and  Climate  Enterprise.”  Society  Report.  Boston,  MA,  2012.  

———.  “The  Bachelor’s  Degree  in  Meteorology  or  Atmospheric  Science.”  Bulletin  of  the  American  Meteorological  Society  68,  no.  12  (1987):  1570.  

———.  “What  Is  a  Meteorologist?  A  Professional  Guideline,”  September  28,  1990.  https://www.ametsoc.org/ams/index.cfm/about-­‐ams/ams-­‐statements/archive-­‐statements-­‐of-­‐the-­‐ams/what-­‐is-­‐a-­‐meteorologist-­‐a-­‐professional-­‐guideline/.  

Anderson,  Ben.  “Preemption,  Precaution,  Preparedness:  Anticipatory  Action  and  Future  Geographies.”  Progress  in  Human  Geography  34,  no.  6  (2010):  777–798.  

Anderson,  Jennings,  Marina  Kogan,  Melissa  Bica,  Leysia  Palen,  Kenneth  Anderson,  Rebecca  Morss,  Julie  L.  Demuth,  Olga  V.  Wilhelmi,  and  Jennifer  Henderson.  “Far  Far  Away  in  Far  Rockaway:  Responses  to  Risks  and  Impacts  during  Hurricane  Sandy  through  First-­‐  Person  Social  Media  Narratives.”  In  Social  Media  Studies.  Rio  de  Janeiro,  Brazil,  2016.  

Anderson,  Katharine.  Predicting  the  Weather:  Victorians  and  the  Science  of  Meteorology.  Chicago:  University  Of  Chicago  Press,  2005.  

Ashley,  W.  S.  “Spatial  Analysis  of  Tornado  Fatalities  in  the  United  States:  1880-­‐2005.”  Weather  and  Forecasting  22  (2007):  1214–28.  

Ashley,  W.  S.,  A.  J.  Krmenec,  and  R.  Schwantes.  “Vulnerability  due  to  Nocturnal  Tornadoes.”  Weather  and  Forecasting  23,  no.  5  (October  2008):  795–807.  doi:10.1175/2008waf2222132.1.  

Bankoff,  Greg,  Georg  Frerks,  and  Dorothea  Hilhorst.  Mapping  Vulnerability:  Disasters,  Development,  and  People.  Routledge,  2004.  http://books.google.com/books?hl=en&lr=&id=qVgBOGzkt0sC&oi=fnd&pg=PR2&dq=Mapping+Vulnerability:+Disasters,+Development+and+People&ots=e41E1RQalo&sig=fxTGCdIbdYneUUA4s1El9tvediM.  

Barnes,  L.  R.,  E.  C.  Gruntfest,  M.  H.  Hayden,  D.  M.  Schultz,  and  C.  Benight.  “False  Alarms  and  Close  Calls:  A  Conceptual  Model  of  Warning  Accuracy.”  Weather  and  Forecasting  22,  no.  5  (October  2007):  1140–47.  doi:10.1175/waf1031.1.  

Bavington,  Dean.  Managed  Annihilation:  An  Unnatural  History  of  the  Newfoundland  Cod  Collapse.  Nature/History/Society.  Vancouver,  BC:  UBC  Press,  2010.  

173

Beck,  Ulrich.  Risk  Society:  Towards  a  New  Modernity.  London:  Sage  Publications,  1992.  Bergman,  Aake,  Jerrold  J.  Heindel,  Susan  Jobling,  Karen  Kidd,  and  Thomas  R.  Zoeller.  “State  

of  the  Science  of  Endocrine  Disrupting  Chemicals  2012:  Summary  for  Decision-­‐Makers.”  World  Health  Organization,  2013.  https://extranet.who.int/iris/restricted/handle/10665/78102.  

Bierbaum,  Rosina,  Joel  Smith,  Arthur  Lee,  Maria  Blair,  Lynne  Carter,  F.  Stuart  Chapin,  Paul  Fleming,  Susan  Ruffo,  Missy  Stults,  and  Shannon  McNeeley.  “A  Comprehensive  Review  of  Climate  Adaptation  in  the  United  States:  More  than  Before,  but  Less  than  Needed.”  Mitigation  Adapt.  Strategies  Global  Change  18,  no.  3  (March  2013):  361–406.  

Bijker,  Wiebe  E.,  Thomas  P.  Hughes,  and  Trevor  J.  Pinch.  The  Social  Construction  of  Technological  Systems:  New  Directions  in  the  Sociology  and  History  of  Technology.  Cambridge,  MA:  MIT  Press,  1987.  

Blaikie,  Piers,  Terry  Cannon,  Ian  Davis,  and  Ben  Wisner.  At  Risk:  Natural  Hazards,  People’s  Vulnerability  and  Disasters.  Routledge,  2004.  http://books.google.com/books?hl=en&lr=&id=4M6AAgAAQBAJ&oi=fnd&pg=PP1&dq=At+Risk:Natural+Hazards,+People%E2%80%99s+Vulnerability+andDisasters&ots=xku889TTja&sig=w8XaogN7cXjF8FzU4DgUV074nR4.  

Bosart,  Lance  F.  “SUNYA  Experimental  Results  in  Forecasting  Daily  Temperature  and  Precipitation.”  Monthly  Weather  Review  103  (November  1975):  1013–20.  

Boser,  Susan.  “Power,  Ethics,  and  the  IRB.”  Qualitative  Inquiry  13,  no.  8  (2007):  1060–74.  Boswell-­‐Penc,  Maia.  Tainted  Milk:  Breastmilk,  Feminisms,  and  the  Politics  of  Environmental  

Degradation.  Albany,  NY:  State  University  of  New  York  Press,  2006.  Bowker,  Geoffrey,  and  Susan  Leigh  Star.  Sorting  Things  out:  Classification  and  Its  

Consequences.  Cambridge:  MIT  Press,  2000.  Braun,  Daniel.  “Sorting  Out  Disasters:  A  New  Case  Study  for  Classification  Theory.”  Scientia  

Canadensis  29,  no.  2006  (2006):  73–86.  Breslin,  Sean.  “10  Things  the  National  Weather  Service  Wants  You  to  Know  about  Winter  

Weather  Forecasts.”  Company  Website.  Winter  Safety  and  Preparedness,  January  22,  2016.  https://weather.com/safety/winter/news/winter-­‐forecasts-­‐nws-­‐state-­‐college.  

Bridenstine,  Jim.  “Private  Sector  Weather  Forecasting:  Assessing  Products  and  Technologies.”  Government.  Washington,  D.C.,  June  8,  2016.  https://science.house.gov/sites/republicans.science.house.gov/files/documents/HHRG-­‐114-­‐SY18-­‐WState-­‐B001283-­‐20160608.pdf.  

Brody,  Julia  Green,  Sarah  C.  Dunagan,  Rachel  Morello-­‐Frosch,  Phil  Brown,  Sharyle  Patton,  and  Ruthann  A.  Rudel.  “Reporting  Individual  Results  for  Biomonitoring  and  Environmental  Exposures:  Lessons  Learned  from  Environmental  Communication  Case  Studies.”  Environmental  Health  13,  no.  40  (2014):  8.  

Brooks,  Harold  E.,  Michael  Fritsch,  and  Charles  A  Doswell  III.  “The  Future  of  Weather  Forecasting:  The  Eras  of  Revolution  and  Reconstruction.”  In  Preprints.  Norfolk,  VA,  1996.  

Brown,  H.  E.,  and  Edwin  B.  Fawcett.  “Use  of  Numerical  Guidance  and  the  National  Weather  Service’s  National  Meteorological  Center.”  Journal  of  Applied  Meteorology  11  (1972):  1175–82.  

174

Brown,  Phil,  R  Morello-­‐Frosch,  Julia  Green  Brody,  Rebecca  Gasior  Altman,  Ruthann  A.  Rudel,  Laura  Senier,  Carla  Perez,  and  Ruth  Simpson.  “Institutional  Review  Board  Challenges  Related  to  Community-­‐Based  Participatory  Research  on  Human  Exposure  to  Environmental  Toxins:  A  Case  Study.”  IRB:  Ethics  &  Human  Research,  Opposing  Views  in  Context,  9,  no.  39  (2010):  1–12.  

Brown,  Vankita  Y.  “Risk  Communication  Across  Cultures:  A  Study  of  the  Impact  of  Social  Context,  Warning  Components,  and  Receiver  Characteristics  on  the  Protective  Action  of  African  Americans.”  Ph.D.,  Mass  Communication  &  Media  Studies,  2011.  

Brun,  Cathrine.  “A  Geographers’  Imperative?  Research  and  Action  in  the  Aftermath  of  Disaster.”  The  Geographical  Journal  175,  no.  3  (2009):  196–207.  

Butler,  Judith.  Gender  Trouble:  Feminism  and  the  Subversion  of  Identity.  New  York:  Routledge,  1990.  

Callon,  Michel.  “Elements  of  a  Sociology  of  Translation:  Domestication  of  the  Scallops  and  the  Fishermen  of  St  Brieuc  Bay.”  In  Power,  Action  and  Belief:  A  New  Sociology  of  Knowledge?,  196–233.  London:  Routledge,  1986.  

Carlson,  Joe.  “Seeking  Shelter:  As  Tornado  Bore  Down,  Residents  Flocked  to  Hospital.”  Industry  Journal  Website.  Modern  Healthcare,  May  25,  2013.  http://www.modernhealthcare.com/article/20130525/MAGAZINE/305259979.  

Clark,  Nigel.  “Geo-­‐Politics  and  the  Disaster  of  the  Anthropocene.”  The  Sociological  Review  62,  no.  S1  (2014):  19–37.  

Cohen,  Benjamin  R.,  and  Wyatt  Galusky.  “Guest  Editorial.”  Science  as  Culture  19,  no.  1  (2010):  1–14.  

Colabuono,  Fernanda,  S  Taniguchi,  and  Rosalinda  C.  Montone.  “Polychlorinated  Biphenyls  and  Organochlorine  Pesticides  in  Plastics  Ingested  by  Seabirds.”  Marine  Pollution  Bulletin  60,  no.  4  (April  2010):  630–34.  

Collins,  Harry,  Robert  Evans,  and  Michael  E.  Gorman.  “Trading  Zones  and  Interactional  Expertise.”  In  Trading  Zones  and  Interactional  Expertise:  Creating  New    Kinds  of  Collaborations,  7–24.  Cambridge,  MA:  MIT  Press,  2010.  

Collins,  Harry  M.,  G.  H.  de  Vries,  and  Wiebe  E.  Bijker.  “Ways  of  Going  On:  An  Analysis  of  Skill  Applied  to  Medical  Practice.”  Science,  Technology,  &  Human  Values  22,  no.  3  (1997):  267–85.  

Collins,  H.M.  “The  TEA  Set:  Tacit  Knowledge  and  Scientific  Networks.”  Science  Studies  4,  no.  2  (1974):  165–85.  

Committee  on  the  Assessment  of  the  National  Weather  Service’s,  and  Committee  on  the  Assessment  of  the  National  Weather  Service’s.  “Weather  Services  for  the  Nation:  Becoming  Second  to  None.”  Washington,  D.C.:  National  Research  Council,  2012.  

Comptroller  General  of  the  United  States.  “Problems  Plague  National  Weather  Service  ADP  System.”  Gathersburg,  MD:  General  Accounting  Office,  November  18,  1981.  

Cone,  Maria.  Silent  Snow:  The  Slow  Poisoning  of  the  Arctic.  New  York  City:  Grove  Press,  2006.  

Correia,  Jr.,  James,  Richard  Smith,  Jack  R.  Friedman,  Joseph  T.  Ripberger,  and  Harold  E.  Brooks.  “Forecasting  and  Response  to  the  20  May  2013  Oklahoma  City  Area  Tornado.”  Conference  Presentation  presented  at  the  American  Meteorological  Society  Annual  Meeting,  Atlanta,  GA,  February  5,  2014.  https://ams.confex.com/ams/94Annual/webprogram/Session35038.html.  

175

Cressman,  George.  Briefing  on  the  National  Oceanic  and  Atmospheric  Administration.  Washington,  D.C.:  U.S.  Government  Printing  Office,  1977.  

Cutter,  Susan.  Hazards  Vulnerability  and  Environmental  Justice.  Earthscan  Publications  Ltd.,  2006.  

Daipha,  Phaedra.  “From  Bricolage  to  Collage:  The  Making  of  Decisions  at  a  Weather  Forecast  Office.”  Sociological  Forum  30,  no.  3  (September  2015):  787–808.  

———.  Masters  of  Uncertainty:  Weather  Forecasters  and  the  Quest  for  Ground  Truth.  Chicago:  University  Of  Chicago  Press,  2015.  

———.  “Weathering  Risk:  Uncertainty,  Weather  Forecasting,  and  Expertise.”  Sociology  Compass  6,  no.  1  (2012):  15–25.  

Daston,  Lorraine.  “Objectivity  and  the  Escape  from  Perspective.”  Social  Studies  of  Science  22,  no.  4  (1992):  597–618.  

Daston,  Lorraine,  and  Peter  Galison.  Objectivity.  New  York:  Zone  Books,  2007.  Dean,  Mitchell.  “Risk,  Calculable  and  Incalculable,”  131–60.  Cambridge:  Cambridge  

University  Press,  1999.  Denzin,  Norman  K.,  and  Michael  D.  Giardina.  Ethical  Futures  in  Qualitative  Research:  

Decolonizing  the  Politics  of  Knowledge.  Walnut  Creek,  CA:  Left  Coast  Press,  2007.  Dewailly,  Eric,  Albert  Nantel,  Jean  P.  Weber,  and  Francois  Meyer.  “High  Levels  of  PCBs  in  

Breast  Milk  of  Inuit  Women  from  Arctic  Quebec.”  Bulletin  of  Environmental  Contamination  and  Toxicology  43,  no.  5  (November  1989):  641–46.  

Dilling,  Lisa,  Meaghan  E.  Daley,  William  R.  Travis,  Olga  V.  Wilhelmi,  and  Roberta  A.  Klein.  “The  Dynamics  of  Vulnerability:  Why  Adapting  to  Climate  Variability  Will  Not  Always  Prepare  Us  for  Climate  Change.”  WIREs  Climate  Change  6  (August  2015):  413–25.  

Dilling,  Lisa,  Kirsten  Lackstrom,  Benjamin  Haywood,  Kirstin  Dow,  Maria  Carmen  Lemos,  John  Berggren,  and  Scott  Kalafatis.  “What  Stakeholder  Needs  Tell  Us  about  Enabling  Adaptive  Capacity:  The  Intersection  of  Context  and  Information  Provision  across  Regions  in  the  United  States.”  Weather,  Climate,  and  Society  7  (2015):  5–17.  

Dixon,  D.P.,  Mercer,  A.  E.,  Choi,  J.  and  Allen,  J.  S.  “Tornado  Risk  Analysis:  Is  Dixie  Alley  an  Extension  of  Tornado  Alley?”  Bulletin  of  the  American  Meteorological  Society,  2011,  433–41.  

Donner,  William  R.  “An  Integrated  Model  of  Risk  Perception  and  Protective  Action:  Public  Response  to  Tornado  Warnings.”  Ph.D.,  Department  of  Sociology  and  Criminal  Justice,  2007.  http://ezproxy.lib.vt.edu:8080/login?url=http://search.proquest.com/docview/304861086?accountid=14826.  

Doppelt,  Gerald.  “What  Sort  of  Ethics  Does  Technology  Require?”  In  Symposium  on  Questioning  Technology  by  Andrew  Feenberg,  5:157–95.  Albuquerque,  NM:  Journal  of  Ethics,  2000.  

Doswell,  Charles  A.  “Weather  Forecasting  by  Humans-­‐-­‐Heuristics  and  Decision  Making.”  Weather  and  Forecasting  19  (2004):  1115–26.  

Doswell  III,  Charles.  “The  Human  Element  in  Weather  Forecasting.”  National  Weather  Digest  11,  no.  2  (1986):  6–18.  

Douglas,  Mary,  and  Aaron  Wildavsky.  Risk  and  Culture.  Berkeley:  University  of  California  Press,  1984.  

176

Downey,  Gary.  “What  Is  Engineering  Studies  For?  Dominant  Practices  and  Scalable  Scholarship.”  Engineering  Studies  1,  no.  155–76  (2009).  

———.  “What  Is  Engineering  Studies  For?  Dominant  Practices  and  Scalable  Scholarship.”  Engineering  Studies  1,  no.  155–76  (2009).  

Downey,  Gary  Lee.  The  Machine  in  Me:  An  Anthropologist  Sits  Among  Computer  Engineers.  New  York:  Routledge,  1998.  

Downey,  Gary  Lee,  and  Joseph  Dumit.  “Locating  and  Intervening.”  In  Cyborgs  and  Citadels:  Anthropological  Interventions  in  Emerging  Sciences  and  Technologies,  5–30.  Santa  Fe:  The  SAR  Press,  1997.  

———.  “What  Is  Engineering  Studies  for?:  Dominant  Practices  and     Scalable  Scholarship.”  Engineering  Studies:  Journal  of  the  International  Network  for  Engineering  Studies  1,  no.  1  (2009):  55–76.  

Downey,  Gary  Lee,  and  Teun  Zuiderent-­‐Jerak.  “Making  and  Doing:  Engagement  and  Reflexive  Learning  in  STS.”  In  STS  Handbook,  edited  by  Ulrike  Felt,  Rayvon  Fouche,  Clark  Miller,  and  Laurel  Smith-­‐Doerr,  3rd  ed.  Cambridge,  Mass:  MIT  Press,  2016.  

Droessler,  Earl  G.  “The  Weather  Forecaster  Today.”  Bulletin  of  the  American  Meteorological  Society  61,  no.  3  (March  1980):  194–95.  

Drucker,  Peter  F.  “The  Promise  of  Automation.”  In  Automation:  Implications  for  the  Future,  215–29.  New  York:  Vintage  Books,  1962.  

Edwards,  Paul  N.  A  Vast  Machine:  Computer  Models,  Climate  Data,  and  the  Politics  of  Global  Warming.  Cambridge:  MIT  Press,  2010.  

———.  “Representing  the  Global  Atmosphere:  Computer  Models,  Data  and  Knowledge  about  Climate  Change.”  In  Changing  the  Atmosphere:  Expert  Knowledge  and  Environmental  Governance,  edited  by  Paul  N.  Edwards  and  Clark  Miller,  31–66.  Cambridge:  MIT  Press,  2001.  

Ellis,  Carolyn.  “Telling  Secrets,  Revealing  Lives:  Relational  Ethics  in  Research  with  Intimate  Others.”  Qualitative  Inquiry  13,  no.  1  (2007):  3–29.  

Elwood,  S.  “Negotiating  Knowledge  Production:  The  Everyday  Inclusions,  Exclusions,  and  Contradictions  of  Participatory  GIS  Research.”  Professional  Geographer  58,  no.  2  (May  2006):  197–208.  doi:10.1111/j.1467-­‐9272.2006.00526.x.  

Endress,  Martin.  “The  Social  Constructedness  of  Resilience.”  Social  Sciences  4  (2015):  533–45.  

Englund,  Eric.  “Forecaster:  We  Erred  on  the  Side  of  Caution.”  The  Sandpaper.  September  16,  2016,  Online  edition.  http://thesandpaper.villagesoup.com/p/forecaster-­‐we-­‐erred-­‐on-­‐the-­‐side-­‐of-­‐caution/1569021.  

Erikson,  Kai.  A  New  Species  of  Trouble:  The  Human  Experience  of  Modern  Disasters.  New  York:  W.  W.  Norton  &  Company,  Inc.,  1995.  

Erikson,  K.T,  and  W.  Yule.  A  New  Species  of  Trouble:  Explorations  in  Disaster,  Trauma,  and  Community.  New  York:  Norton,  1994.  

Fawcett,  Edwin  B.  “Six  Years  of  Operational  Numerical  Weather  Prediction.”  Journal  of  Applied  Meteorology  1  (September  1962):  318–32.  

Fawcett,  Stephen  B.  “Some  Values  Guiding  Community  Research  and  Action.”  Journal  of  Applied  Behavior  Analysis  24,  no.  4  (1991):  621–36.  

Field,  Christopher  B.,  Vicente  R.  Barros,  David  Jon  Dokken,  Katharine  J.  Mach,  and  Michael  Mastrandrea.  “Climate  Change  2014:  Impacts,  Adaptation,  and  Vulnerability.”  

177

Working  Group  II.  Part  A:  Global  and  Sectoral  Aspects.  Cambridge  University  Press:  Intergovernmental  Panel  on  Climate  Change,  2014.  

Fine,  Gary  Alan.  Authors  of  the  Storm:  Meteorologists  and  the  Culture  of  Prediction.  Chicago:  University  Of  Chicago  Press,  2010.  

———.  Authors  of  the  Storm:  Meteorologists  and  the  Culture  of  Prediction.  Chicago,  Ill.;  Bristol:  University  Of  Chicago  Press,  2010.  

Fisher,  Erik.  “Ethnographic  Invention:  Probing  the  Capacity  of  Laboratory  Decisions.”  NanoEthics  1,  no.  2  (2007):  155–165.  

———.  “Public  Science  and  Technology  Scholars:  Engaging  Whom?”  Science  &  Engineering  Ethics  17  (2011):  607–620.  

Fleming,  James  Rodger.  Fixing  the  Sky:  The  Checkered  History  of  Weather  and  Climate  Control.  New  York:  Columbia  University  Press,  2012.  

Forbes,  Greg.  “What  and  Where  Is  Tornado  Alley?,”  2005.  http://www.weather.com/outlook/weather-­‐news/news/articles/what-­‐where-­‐is-­‐tornado-­‐alley-­‐forbes_2011-­‐04-­‐14.  

Fortun,  Kim.  Advocacy  after  Bhopal:  Environmentalism,  Disaster,  New  Global  Orders.  Chicago:  University  Of  Chicago  Press,  2001.  

Fortun,  Kim,  Scott  Gabriel  Knowles,  Vivian  Choi,  Paul  Jobin,  Miwao  Matsumoto,  Pedro  de  la  Torre,  and  Max  Liboiron.  “Disaster  STS.”  In  STS  Handbook,  edited  by  Ulrike  Felt,  Rayvon  Forche,  Clark  Miller,  and  laurel  Smith-­‐Doerr.  Boston:  MIT  Press,  2017.  

Fothergill,  Alice,  and  Lori  Peek.  “Poverty  and  Disasters  in  the  United  States:  A  Review  of  Recent  Sociological  Findings.”  Natural  Hazards  32,  no.  1  (2004):  89–110.  

Foucault,  Michel.  “Governmentality.”  In  The  Foucault  Effect:  Studies  in  Governmentality,  87–105.  Chicago:  University  Of  Chicago  Press,  1991.  

———.  Language,  Counter-­‐Memory,  Practice:  Selected  Essays  and  Interviews.  Ithaca,  NY:  Cornell  University  Press,  1980.  

Frates,  Michael.  “Demystifying  Colloquial  Tornado  Alley:  Delineation  of  New  Tornado  Alleys  in  the  Central  and  Eastern  United  States.”  Akron:  University  of  Akron,  2011.  http://www.uakron.edu/im/online-­‐newsroom/news_details.dot?newsId=1086649&pageTitle=UA%20News&crumbTitle=Should+’Tornado+Alley’+shift+south%3F.  

Frickel,  Scott,  Sahra  Gibbon,  Jeff  Howard,  Joanna  Kempner,  Gwen  Ottinger,  and  David  J.  Hess.  “Undone  Science:  Charting  Social  Movement  and  Civil  Society  Challenges  to  Research  Agenda  Setting.”  Science,  Technology,  &  Human  Values  35,  no.  4  (2010):  444–73.  

Friday,  Elbert  W.  “The  Modernization  and  Associated  Restructuring  of  the  National  Weather  Service:  An  Overview.”  Bulletin  of  the  American  Meteorological  Society  75,  no.  1  (January  1994):  44–52.  

Friedman,  Robert  Marc.  Appropriating  the  Weather:  Vilhelm  Bjerknes  and  the  Modern  Construction  of  a  Modern  Meteorology.  London:  Cornell  University  Press,  1989.  

Fujita,  Tetsuya  T.  “A  Proposed  Characterization  of  Tornadoes  and  Hurricanes  by  Area  and  Intensity.”  SMRP  Paper.  Chicago:  University  of  Chicago,  1971.  

Fuller,  Steve.  “Constructing  the  High  Church-­‐Low  Church  Distinction  in  STS  Textbooks.”  Bulletin  of  Science,  Technology  &  Society  17  (1997):  181–83.  

———.  “The  Future  of  Science  and  Technology  Studies.”  In  New  Frontiers  in  Science  and  Technology  Studies,  205–17.  New  York:  Wiley,  2007.  

178

Furgione,  Laura.  Weather  Ready  Nation  and  Social  Sciences,  March  13,  2012.  Gabor,  Dennis.  “Inventing  the  Future.”  In  Automation:  Implications  for  the  Future,  edited  by  

Morris  Philipson,  132–61.  New  York:  Vintage  Books,  1962.  Gagan,  John  P.,  Alan  Gerard,  and  John  Gordon.  “A  Historical  and  Statistical  Comparison  of  

‘Tornado  Alley’  to  ‘Dixie  Alley.’”  National  Weather  Digest  34,  no.  2  (2010):  145–55.  Gall,  Melanie,  Khai  Hoan  Nguyen,  and  Susan  L.  Cutter.  “Integrated  Research  on  Disaster  

Risk:  Is  It  Really  Integrated.”  International  Journal  of  Disaster  Risk  Reduction  12  (June  2015):  255–67.  

Gelman,  Andrew.  “What  Do  Rick  Santorum  and  Andrew  Cuomo  Have  in  Common?”  News  Outlet.  The  Washington  Post,  November  24,  2014.  https://www.washingtonpost.com/news/monkey-­‐cage/wp/2014/11/24/what-­‐do-­‐rick-­‐santorum-­‐and-­‐andrew-­‐cuomo-­‐have-­‐in-­‐common/.  

Glahn,  Harry  R.  “Computer  Worded  Forecasts.”  Bulletin  of  the  American  Meteorological  Society  60,  no.  1  (January  1979):  4–11.  

———.  “On  MOS  and  Perfect  Prog  for  Interpretive  Guidance.”  Technical  Memorandum.  U.S.  Department  of  Commerce,  July  18,  1991.  

———.  “Progress  in  the  Automation  of  Public  Weather  Forecasts.”  Monthly  Weather  Review  1504–1511  (December  1976):  1505.  

Goldberg,  Arthur.  “The  Challenge  of  ‘Industrial  Revolution  II.’”  In  Automation:  Implications  for  the  Future,  3–12.  New  York:  Vintage  Books,  1962.  

Goodsell,  Charles  T.  “U.S.  National  Weather  Service.”  In  Mission  Mystique:  Belief  Systems  in  Public  Agencies,  64–95.  Washington,  D.C.:  CQ  Press,  2011.  

Grun,  Felix,  and  Bruce  Blumberg.  “Endocrine  Disrupters  as  Obesogens.”  Molecular  and  Cellular  Endocrinology,  Special  Issue:  Endocrine  Disruptors  from  the  Environment  in  the  Aetiology  of  Obesity  and  Diabetes  304,  no.  1–2  (2009):  19–29.  

Gruntfest,  Eve  C.,  Lindsey  R.  Barnes,  Mary  H.  Hayden,  David  M.  Shultz,  and  Charles  Benight.  “False  Alarms  and  Close  Calls:  A  Conceptual  Model  of  Warning  Accuracy.”  Weather  and  Forecasting  22  (October  2007):  1140–47.  

Gusterson,  Hugh.  Nuclear  Rites:  A  Weapons  Laboratory  at  the  End  of  the  Cold  War.  Berkeley:  University  of  California  Press,  1996.  

———.  “Studying  Up  Revisited.”  PoLAR:  Political  and  Legal  Anthropology  20,  no.  1  (1997):  114–19.  

Halden,  Rolf  U.  “Plastics  and  Health  Risks.”  Annual  Review  of  Public  Health  31,  no.  1  (2010):  179–94.  

Hale,  Charles  R.  “Activist  Research  v.  Cultural  Critique:  Indigenous  Land  Rights  and  the  Contradictions  of  Politically  Engaged  Anthropology.”  Cultural  Anthropology  21,  no.  1  (2006):  96–120.  

Halvorsen,  Sam.  “Militant  Research  Against-­‐and-­‐Beyond  Itself:  Critical  Perspectives  from  the  University  and  Occupy  London.”  Area  47  (2015):  466–72.  

Hamilton,  Clive.  “Ethical  Anxieties  about  Geoengineering:  Moral  Hazard,  Slippery  Slope  and  Playing  God.”  Technology,  2010.  http://www.see.ed.ac.uk/~shs/Climatechange/Geo-­‐politics/ethical_anxieties_about_geoengineering.pdf.  

Haraway,  D.  J.  “Anthropocene,  Capitalocene,  Chthulucene:  Staying  with  the  Trouble.”  Presentation  at  the  symposium  “Anthropocene:  Arts  of  Living  on  a  Damaged  Planet”.  Santa  Cruz,  California.  May,  2014.  

179

Haraway,  Donna.  “A  Manifesto  for  Cyborgs:  Science,  Technology,  and  Socialist  Feminism  in  the  1980s.”  In  Feminism  /  Postmodernism,  edited  by  Linda  J.  Nicholson,  190–233.  New  York:  Routledge,  1990.  

———.  “Situated  Knowledges:  The  Science  Question  in  Feminism  and  the  Privilege  of  Partial  Perspective.”  In  Simians,  Cyborgs,  and  Women:  The  Reinvention  of  Nature,  183–203.  New  York:  Routledge,  1991.  

Harding,  Sandra.  Objectivity  and  Diversity:  Another  Logic  of  Science.  University  Of  Chicago  Press,  2015.  

———.  “Rethinking  Standpoint  Epistemology:  What  Is  ‘Strong  Objectivity’?”  In  Feminist  Epistemologies,  edited  by  Linda  Alcoff  and  Elizabeth  Potter.  New  York:  Routledge,  2005.  

———.  “Standpoint  Theories:  Productively  Controversial.”  Hypatia  24,  no.  4  (2009):  192–200.  

Harned,  Steve.  “NWA  History.”  National  Weather  Association,  2016.  http://nwas.org/about-­‐nwa/nwa-­‐history/.  

Healy,  Stephen,  and  Jessica  Mesman.  “Resilience:  Contingency,  Complexity,  and  Practice.”  In  Vulnerability  in  Technological  Cultures:  New  Directions  in  Research  and  Governance,  edited  by  Anique  Hommels,  Jessica  Mesman,  and  Wiebe  E.  Bijker,  155–77.  Cambridge,  MA:  MIT  Press,  2014.  

Heidegger,  Martin.  “The  Question  Concerning  Technology.”  In  The  Question  Concerning  Technology  and  Other  Essays,  3–35.  New  York:  Harper  &  Row,  1977.  

Held,  Virginia.  The  Ethics  of  Care:  Personal,  Political,  and  Global.  Oxford,  England:  Oxford  University  Press,  2006.  

Hill,  Jerry  D,  and  Gerald  D.  Mulvey.  “Business  Ethics  for  Professional  Meteorology:  Expectation  and  Satisfied  Customers.”  Bulletin  of  the  American  Meteorological  Society,  June  2012,  889–91.  

———.  “Resources  and  Guidance  for  Ethics  and  Personal  Conduct  in  Meteorology.”  Bulletin  of  the  American  Meteorological  Society,  January  2014,  164–67.  

Hill,  Jerry  D,  and  Gerald  J.  Mulvey.  “The  Ethics  of  Defining  a  Professional:  Who  Is  a  Meteorologist?”  Bulletin  of  the  American  Meteorological  Society  93,  no.  7  (July  2012):  1080–82.  

Hoekstra,  Stephanie,  Kimberly  Klockow,  R  Riley,  J  Brotzgge,  Harold  Brooks,  and  Somer  Erikson.  “A  Preliminary  Look  at  the  Social  Perspective  of  Warn-­‐on-­‐Forecast:  Preferred  Tornado  Warning  Lead  Time  and  the  General  Public’s  Perceptions  of  Weather  Risks.”  Weather,  Climate,  and  Society  3  (April  2011):  129–40.  

Hughes,  Patrick.  Century  of  Weather  Service:  A  History  of  the  Birth  and  Growth  of  the  National  Weather  Service  1870-­‐1970.  Gordon  &  Breach,  1970.  

Hughes,  Thomas  P.  “The  Evolution  of  Large  Technological  Systems.”  In  The  Social  Construction  of  Technological  Systems,  edited  by  Wiebe  E.  Bijker  and  Thomas  P.  Hughes,  51–82.  Cambridge,  MA:  MIT  Press,  1987.  

Hulme,  Mike.  Can  Science  Fix  Climate  Change?  A  Case  against  Climate  Engineering.  Cambridge:  Polity  Press,  2014.  

Humphreys,  David.  “Smoke  and  Mirrors:  Some  Reflections  on  the  Science  and  Politics  of  Geoengineering.”  The  Journal  of  Environment  &  Development  20,  no.  2  (June  1,  2011):  99–120.  doi:10.1177/1070496511405302.  

180

Huntsville-­‐Madison  County  Emergency  Management  Agency.  “Alabama  Tornado  Outbreak,”  2011.  http://w4hmc.wordpress.com/2011/05/18/alabama-­‐tornado-­‐outbreak/.  

Jankovic,  Vladimir,  Deborah  R.  Coen,  and  James  Rodger  Fleming.  Intimate  Universality:  Local  and  Global  Themes  in  the  History  of  Weather  and  Climate.  Sagamore  Beach:  Science  History  Publications,  2006.  

Jasanoff,  Sheila.  “Future  Imperfect:  Science,  Technology,  and  the  Imaginations  of  Modernity.”  In  Dreamscapes  of  Modernity:  Sociotechnical  Imaginaries  and  the  Fabrication  of  Power,  edited  by  Kim  Sang-­‐Hyun  and  Sheila  Jasanoff,  1–32.  Chicago:  University  Of  Chicago  Press,  2015.  

———.  “Imagined  and  Invented  Worlds.”  In  Dreamscapes  of  Modernity:  Sociotechnical  Imaginaries  and  the  Fabrication  of  Power,  edited  by  Sheila  Jasanoff  and  Kim  Sang-­‐Hyun,  363.  Chicago:  University  Of  Chicago  Press,  2015.  

Jensen,  C.  E.  “A  Review  of  Federal  Meteorological  Programs  for  Fiscal  Years  1965-­‐1975.”  Bulletin  of  the  American  Meteorological  Society  56  (1975):  208–24.  

Jonas,  H.  “Technology  and  Responsibility.”  In  Readings  in  the  Philosophy  of  Technology,  173–84.  Lanham,  MD:  Rowman  &  Littlefield  Publishers,  Inc,  2009.  

Jonas,  Hans.  “Toward  a  Philosophy  of  Technology.”  Hastings  Center  Report,  February  1979,  34–43.  

Kalnay,  Eugenia,  and  Amnon  Dalcher.  “Forecasting  Forecast  Skill.”  Monthly  Weather  Review  115  (February  1987):  349–56.  

Keller,  Evelyn  Fox,  and  Elisabeth  Anne  Lloyd.  “Introduction.”  In  Keywords  in  Evolutionary  Biology,  395.  Cambridge,  MA:  Harvard  University  Press,  1992.  

Klein,  William  H.  “Objective  Forecasts  of  Surface  Temperature  from  One  to  Three  Days  in  Advance.”  Journal  of  Applied  Meteorology  5,  no.  2  (April  1966):  137–47.  

———.  “The  Computer’s  Role  in  Weather  Forecasting.”  Weatherwise  22,  no.  5  (1969):  195–218.  

Knowles,  Scott  Gabriel.  The  Disaster  Experts:  Mastering  Risk  in  Modern  America.  Philadelphia:  University  of  Pennsylvania  Press,  2011.  

———.  The  Disaster  Experts:  Mastering  Risk  in  Modern  America.  Philadelphia:  University  of  Pennsylvania  Press,  2013.  

Kobayashi,  Audrey.  “Coloring  the  Field:  Gender,  ‘Race,’  and  the  Politics  of  Fieldwork.”  The  Professional  Geographer  46,  no.  1  (1994):  73–80.  

Langston,  Nancy.  Toxic  Bodies:  Hormone  Disruptors  and  the  Legacy  of  DES.  New  Haven,  CT:  Yale  University  Press,  2010.  

Lasorsa,  Brian.  “Verification  Statistics.”  PowerPoint  presented  at  the  3rd  Biennial  Emergency  Management  /  Broadcast  Media  Conference,  Baltimore,  MD,  2014.  http://www.weather.gov/media/lwx/em/prsnt/verification.pdf.  

Latour,  Bruno.  Aramis,  or  the  Love  of  Technology.  Cambridge,  MA:  Harvard  University  Press,  1996.  

———.  Reassembling  the  Social:  An  Introduction  to  Actor-­‐Network-­‐Theory.  Oxford:  Oxford  University  Press,  2007.  

———.  Science  in  Action:  How  to  Follow  Scientists  and  Engineers  through  Society.  Cambridge:  Harvard  University  Press,  1987.  

———.  “Why  Has  Critique  Run  Out  of  Steam?  From  Matters  of  Fact  to  Matters  of  Concern.”  Critical  Inquiry  30  (2004):  225–48.  

181

Lavell,  A.,  M.  Oppenheimer,  C.  Diop,  J.  Hess,  R.  Lempert,  J.  Li,  R.  Muir-­‐Wood,  and  S.  Myeong.  “Climate  Change:  New  Dimensions  in  Disaster  Risk,  Exposure,  Vulnerability,  and  Resilience.”  Managing  the  Risks  of  Extreme  Events  and  Disasters  to  Advance  Climate  Change  Adaptation,  2012,  25–64.  

Layman,  John.  Personal  Interview.  Audio  Recording,  April  2008.  Lazo,  Jeffrey  K.,  Rebecca  E.  Morss,  and  Julie  L.  Demuth.  “300  Billion  Served:  Sources,  

Perceptions,  Uses,  and  Values  of  Weather  Forecasts.”  Bulletin  of  the  American  Meteorological  Society,  no.  6  (2009):  785–98.  

Lazrus,  Heather,  Betty  Morrow,  Rebecca  Morss,  and  Jeffrey  K.  Lazo.  “Vulnerability  beyond  Stereotypes:  Context  and  Agency  in  Hurricane  Risk  Communication.”  Weather,  Climate,  and  Society  4  (April  2012):  103–9.  

LeFebvre,  Thomas.  Development  of  AWIPS.  In-­‐person,  June  7,  2015.  Lehmann,  James  L.  “AFOS:  The  AFOS  Working  Environment.”  National  Weather  Digest  4,  no.  

1  (1979):  1–5.  LeMone,  Margaret  A.,  and  Patricia  L.  Waukau.  “Women  in  Meteorology.”  Bulletin  of  the  

American  Meteorological  Society  63,  no.  11  (1982):  1266–76.  “Lesson  of  the  Day.”  Indigenous  Nation  Website.  School  of  Choctaw  Language,  2013.  

http://choctawschool.com/media/92612/lessons_noun_mahli_chito_-­‐_tornado.pdf.  Lewis,  Adam  Gary.  “Ethics,  Activism  and  the  Anti-­‐Colonial:  Social  Movement  Research  as  

Resistance.”  Social  Movement  Studies  11,  no.  2  (2012):  227–40.  Liboiron,  Max.  “Disaster  Data,  Data  Activism:  Grassroots  Responses  to  Representations  of  

Superstorm  Sandy.”  In  Extreme  Weather  and  Global  Media,  144–62.  New  York:  Routledge,  2015.  

———.  “Disaster  Data,  Data  Activism:  Grassroots  Responses  to  Representations  of  Superstorm  Sandy.”  In  Extreme  Weather  and  Global  Media,  edited  by  Diana  Negra  and  Leyda.  Routledge,  2015.  

———.  “Redefining  Pollution  and  Action:  The  Matter  of  Plastics.”  Journal  of  Material  Culture  21,  no.  1  (2015):  87–110.  

Lochlann,  Jain.  Malignant:  How  Cancer  Becomes  Us.  Berkeley:  University  of  California  Press,  2013.  

Longino,  Helen  E.  “How  Values  Can  Be  Good  for  Science.”  In  Science,  Values,  and  Objectivity,  edited  by  Peter  Machamer,  127–42.  Pittsburgh:  University  of  Pittsburgh  Press,  2004.  

Lopate,  Phillip.  The  Art  of  the  Personal  Essay:  An  Anthology  from  the  Classical  Era  to  the  Present.  New  York:  Anchor  Books,  1995.  

Lövbrand,  Eva,  Johannes  Stripple,  and  Bo  Wiman.  “Earth  System  Governmentality:  Reflections  on  Science  in  the  Anthropocene.”  Global  Environmental  Change  19,  no.  1  (February  2009):  7–13.  doi:10.1016/j.gloenvcha.2008.10.002.  

Lowe,  Thomas,  Katrina  Brown,  Suraje  Dessai,  Miguel  de  FranÁa  Doria,  Kat  Haynes,  and  Katharine  Vincent.  “Does  Tomorrow  Ever  Come?  Disaster  Narrative  and  Public  Perceptions  of  Climate  Change.”  Public  Understanding  of  Science  15,  no.  2006  (2006):  435–57.  

Lusk,  C.M.,  T.R.  Stewart,  K.R.  Hammond,  and  R.J.  Potts.  “Judgment  and  Decision  Making  in  Dynamic  Tasks:  The  Case  of  Forecasting  and  Microburst.”  Weather  and  Forecasting  5  (1990):  627–639.  

Lynch,  Peter.  “The  Origins  of  Computer  Weather  Prediction  and  Climate  Modeling.”  Journal  of  Computational  Physics  227  (2008):  3431–44.  

182

Lyng,  Stephen.  “Edgework:  A  Social  Psychological  Analysis  of  Voluntary  Risk  Taking.”  American  Journal  of  Sociology  95,  no.  4  (1990):  851–86.  

MacDonald,  Alexander.  “Leonard  W.  Snellman,  1920-­‐1999.”  Bulletin  of  the  American  Meteorological  Society  81,  no.  4  (2000):  847.  

MacKenzie,  Donald.  Inventing  Accuracy:  A  Historical  Sociology  of  Nuclear  Missile  Guidance.  Boston,  MA:  The  MIT  Press,  1993.  

Marsh,  Patrick.  “Population  of  NWS  WFOs.”  Personal  Website,  February  19,  2013.  http://tweetimgs.pmarshwx.com/pmarshwx/20130219/20130219192551.jpg.  

Mass,  Cliff.  “Do  We  Need  Local  National  Weather  Service  Offices  If  We  Have  Weather  Apps,  Accuweather,  and  the  Weather  Channel.”  Personal  Website.  Cliff  Mass  Weather  Blog,  June  11,  2016.  http://cliffmass.blogspot.com/2016/06/do-­‐we-­‐need-­‐local-­‐national-­‐weather.html.  

Mato,  Yukie,  Tomohiko  Isobe,  Hideshige  Takada,  Haruyuki  Kanehiro,  Chiyoko  Ohake,  and  Tsuguchika  Kaminuma.  “Plastic  Resin  Pellets  as  a  Transport  Medium  for  Toxic  Chemicals  in  the  Marine  Environment.”  Environmental  Science  &  Technology  35,  no.  2  (2001):  318–24.  

Meisner,  Bernard  M,  Jerry  D  Hill,  and  Gerald  D.  Mulvey.  “Ethics  for  Government  Meteorologists.”  Bulletin  of  the  American  Meteorological  Society,  September  2013,  1420–23.  

Merton,  Robert  K.  “The  Normative  Structure  of  Science.”  In  The  Sociology  of  Science:  Theoretical  and  Empirical  Investigations,  edited  by  Norman  W.  Storer,  267–78.  Chicago:  The  University  of  Chicago  Press,  1973.  

Mileti,  Dennis  S.,  and  Colleen  Fitzpatrick.  Communication  of  Public  Risk:  Its  Theory  and  Its  Application,  1991.  

Mileti,  D.S.,  and  P.W.  O’Brien.  “Warnings  During  Disaster:  Normalizing  Communicated  Risk.”  Social  Problems  39  (1992):  40–57.  

Miller,  Fiona,  Henny  Osbahr,  Emily  Boyd,  Frank  Thomalla,  Sukaina  Bharwani,  Gina  Ziervogel,  Brian  Walker,  et  al.  “Resilience  and  Vulnerability:  Complementary  or  Conflicting  Concepts?”  Ecology  and  Society  15,  no.  3  (2010).  ://WOS:000283867400035.  

Mitroff,  Ian.  “Norms  and  Counter-­‐Norms  in  a  Select  Group  of  the  Apollo  Moon  Scientists:  A  Case  Study  of  the  Ambivalence  of  Scientists.”  American  Sociological  Review  39,  no.  4  (1974):  579–95.  

Morello-­‐Frosch,  R,  J.  Varshavsky,  Max  Liboiron,  P.  Brown,  and  Julia  Green  Brody.  “Communicating  Results  in  Post-­‐Belmont  Era  Biomonitoring  Studies:  Lessons  from  Genetics  and  Neuroimaging  Research.”  Environmental  Research  136  (2015):  363–72.  

Morss,  Rebecca  E.  “Problem  Definition  in  Atmospheric  Science  Public  Policy:  An  Example  of  Observing-­‐System  Design  for  Weather  Prediction.”  Bulletin  of  the  American  Meteorological  Society,  February  2005,  181–91.  

Morss,  Rebecca  E.,  Julie  L.  Demuth,  Ann  Bostrom,  Jeffrey  K.  Lazo,  and  Heather  Lazrus.  “Flash  Flood  Risks  and  Warning  Decisions:  A  Mental  Models  Study  of  Forecasters,  Public  Officials,  and  Media  Broadcasters  in  Boulder,  Colorado.”  Risk  Analysis,  2015.  

———.  “Flash  Flood  Risks  and  Warning  Decisions:  A  Mental  Models  Study  of  Forecasters,  Public  Officials,  and  Media  Broadcasters  in  Boulder,  Colorado.”  Risk  Analysis  35,  no.  11  (2015):  2009–28.  

183

Morss,  Rebecca  E.,  Julie  L.  Demuth,  and  Jeffrey  K.  Lazo.  “Communicating  Uncertainty  in  Weather  Forecasts:  A  Survey  of  the  U.S.  Public.”  Weather  and  Forecasting  23  (2008):  974–91.  

Morss,  Rebecca  E.,  Jeffrey  K.  Lazo,  and  Julie  L.  Demuth.  “Examining  the  Use  of  Weather  Forecasts  in  Decision  Scenarios:  Results  from  a  US  Survey  with  Implications  for  Uncertainty  Communication.”  Meteorological  Applications  17,  no.  2  (June  2010):  149–62.  

Morss,  Rebecca  E.,  Kelsey  J.  Mulder,  Jeffrey  K.  Lazo,  and  Julie  L.  Demuth.  “How  Do  People  Perceive,  Understand,  and  Anticipate  Responding  to  Flash  Flood  Risks  and  Warnings?  Results  from  a  Public  Survey  in  Boulder,  Colorado,  USA.”  Journal  of  Hydrology  in  press  (2016).  

Morss,  Rebecca  E.,  Olga  V.  Wilhelmi,  Gerald  A.  Meehl,  and  Lisa  Dilling.  “Improving  Societal  Outcomes  of  Extreme  Weather  in  a  Changing  Climate:  An  Integrated  Perspective.”  Annual  Review  of  Environment  and  Resources  36  (2011):  1–25.  

Mulkay,  Michael  J.  “Norms  and  Ideology  in  Science.”  Social  Science  Information  15,  no.  4  (1976):  637–56.  

Murphy,  Allan  H.  “What  Is  a  Good  Forecast?  An  Essay  on  the  Nature  of  Goodness  in  Weather  Forecasting.”  Weather  and  Forecasting  8  (June  1993):  281–93.  

Murray,  Iain,  and  David  Bier.  “Do  We  Really  Need  a  National  Weather  Service?”  Commercial  Website.  Fox  New  Opinion,  August  27,  2011.  http://www.foxnews.com/opinion/2011/08/27/do-­‐really-­‐need-­‐national-­‐weather-­‐service.html.  

Nadar,  Laura.  “Up  the  Anthropologist:  Perspectives  Gained  from  Studying  up.”  In  Reinventing  Anthropology,  edited  by  Dell  Hymes,  284–311.  New  York:  Pantheon  Books,  1972.  

Nap,  J.  L.,  H.  M.  Van  den  Dool,  and  J.  Oerlemans.  “A  Verification  of  Monthly  Weather  Forecasts  in  the  Seventies.”  Monthly  Weather  Review  109  (February  1981):  306–12.  

National  Academy  of  Public  Administration.  “Forecast  for  the  Future:  Assuring  the  Capacity  of  the  National  Weather  Service.”  Congressional  Report.  Washington,  D.C.:  National  Academy  of  Public  Administration,  May  2013.  

National  Oceanic  and  Atmospheric  Administration.  “History  of  the  National  Weather  Service.”  Government  site.  National  Weather  Service,  2013.  http://www.weather.gov/timeline.  

———.  “National  Implementation  Plan  for  the  Modernization  and  Associated  Restructuring  of  the  National  Weather  Service.”  Washington,  D.C.:  Department  of  Commerce,  1990.  

———.  “National  Weather  Service  Policy  Directive  1-­‐10:  Managing  the  Provision  of  Environmental  Information.”  Department  of  Commerce,  December  22,  2008.  http://www.nws.noaa.gov/directives/sym/pd00110curr.pdf.  

———.  “NOAA’s  National  Weather  Service.”  Government  site,  January  2002.  http://www.publicaffairs.noaa.gov/grounders/nws.html.  

———.  “NWS  Central  Region  Service  Assessment:  Joplin,  Missouri,  Tornado-­‐-­‐May  22,  2011.”  Service  Assessment.  Kansas  City,  MO:  U.S.  Department  of  Commerce,  2011.  

National  Research  Council.  “A  Vision  for  the  National  Weather  Service:  Road  Map  for  the  Future.”  Washington,  D.C.:  National  Academy  of  Sciences,  1999.  

———.  “Fair  Weather:  Effective  Partnerships  in  Weather  and  Climate  Services.”  Government.  Washington,  D.C.:  National  Academy  of  Sciences,  2003.  

184

———.  “Saragosa,  Texas,  Tornado  May  22,  1987:  An  Evaluation  of  the  Warning  System.”  NWS  Service  Assessment.  Washington,  D.C.:  National  Academy  of  Sciences,  1991.  

National  Science  Board.  “Patterns  and  Perspectives  in  Environmental  Science.”  Government.  Washington,  D.C.:  National  Science  Foundation,  1972.  

National  Weather  Service.  “Eyes  on  the  Sky:  A  Day  in  the  Life  of  an  Incident  Meteorologist  (IMET)  on  the  Front  Lines  of  a  Wildfire.”  Government  site.  Weather  Ready  Nation.  Accessed  May  10,  2016.  http://www.nws.noaa.gov/com/weatherreadynation/imet_article.html.  

———.  “May  20,  2013:  Newcastle,  Moore  Tornado.”  PowerPoint,  Norman,  OK,  May  21,  2013.  http://www.srh.noaa.gov/images/oun/wxevents/20130520/products_presentation.pdf.  

———.  NWS  Huntsville:  A  Look  Back  on  the  April  27th  Outbreak-­‐-­‐Part  1.  You  Tube.  Huntsville,  AL,  2012.  https://www.youtube.com/watch?v=3aP_MgsTfyI.  

———.  “NWS  Strategic  Planning  and  Policy,”  March  29,  2012.  http://www.nws.noaa.gov/sp/.  

———.  “NWS  Support  for  Special  Events.”  Government.  Washington,  D.C.:  National  Oceanic  and  Atmospheric  Administration,  July  14,  2009.  http://www.nws.noaa.gov/directives/sym/pd01018006curr.pdf.  

———.  “Storm  Events  Database.”  Government  site.  National  Centers  for  Environmental  Information.  Accessed  October  6,  2016.  https://www.ncdc.noaa.gov/stormevents/.  

———.  “The  Historic  Tornadoes  of  April  2011.”  Service  Assessment.  Silver  Spring,  Maryland:  Department  of  Commerce,  December  2011.  

———.  “Vision  2005:  National  Weather  Service  Strategic  Plan  for  Weather,  Water,  and  Climate  Services,  2000  -­‐  2005.”  Government.  Washington,  D.C.:  National  Oceanic  and  Atmospheric  Administration,  August  1999.  

———.  “Weather  Ready  Nation  Roadmap.”  Government.  Silver  Spring,  Maryland:  National  Oceanic  and  Atmospheric  Administration,  2013.  

———.  “Working  Together  to  Save  Lives:  National  Weather  Service  Strategic  Plan  for  2005-­‐2010.”  Government.  Washington,  D.C.:  National  Oceanic  and  Atmospheric  Administration,  2005.  

National  Weather  Service  Act  of  1978.  Washington,  D.C.,  1978.  National  Weather  Service  Modernization  Committee.  “An  Assessment  of  the  Advanced  

Weather  Interactive  Processing  System:  Operational  Test  and  Evaluation  of  the  First  System  Build.”  Washington,  D.C.:  National  Research  Council,  1997.  

Nebeker,  Frederick.  Calculating  the  Weather:  Meteorology  in  the  20th  Century.  London:  Academic  Press,  Inc,  1995.  

Nelson,  G.,  J.  Ochocka,  K.  Griffin,  and  J.  Lord.  “‘Nothing  about  Me,  without  Me’:  Participatory  Action  Research  with  Self-­‐Help/Mutual  Aid  Organizations  for  Psychiatric  Consumer/Survivors.”  American  Journal  of  Community  Psychology  26,  no.  6  (1998):  881–912.  

Nixon,  Rob.  Slow  Violence  and  the  Environmentalism  of  the  Poor.  Harvard  University  Press,  2011.  

———.  “Slow  Violence,  Gender,  and  the  Environmentalism  of  the  Poor.”  Journal  of  Commonwealth  and  Postcolonial  Studies  Vols  13,  no.  14.1  (2007):  2006–2007.  

185

Noble,  David  F.  Forces  of  Production:  A  Social  History  of  Industrial  Automation.  New  York:  Knopf,  1984.  

Northwestern  Mutual.  “People  Plus  Technology.”  Planning  and  Progress  Study.  Milwaukee,  WI:  Northwestern  Mutual  Life  Insurance  Company,  2016.  

Nowotny,  Helga,  Peter  Scott,  and  Michael  Gibbons.  “‘Mode  2’  Revisited:  The  New  Production  of  Knowledge.”  Minerva  41,  no.  3  (2003):  179–94.  

NWS  Norman  Forecast  Office  /  May  20,  2013.  You  Tube.  Norman,  OK,  2013.  https://www.youtube.com/watch?v=8FM27KGxCJw.  

NWS  Vision  for  IDSS.  YouTube.  National  Weather  Service  IDSS  Webinar,  2016.  https://www.youtube.com/watch?v=SWbYnqrlt0A&feature=youtu.be.  

Nygreen,  K.  “Reproducing  or  Challenging  Power  in  the  Questions  We  Ask  and  the  Methods  We  Use:  A  Framework  for  Activist  Research  in  Urban  Education.”  The  Urban  Review  38,  no.  1  (2006):  1–26.  

Office  of  Inspections  and  Program  Evaluations.  “NWS’s  Verification  System  for  Severe  and  Hazardous  Weather  Forecasting  Needs  Modernization.”  Government.  Washington,  D.C.:  National  Oceanic  and  Atmospheric  Administration,  1998.  

O’Malley,  Patrick.  “Risk  and  Responsibility.”  In  Foucault  and  Political  Reason~  Liberalism,  Neo-­‐Liberalism  and  Rationalities  of  Government,  edited  by  Andrew  Barry,  Thomas  Osborne,  and  Nikolas  Rose,  189–207.  Chicago:  University  Of  Chicago  Press,  1996.  

Otway,  Harry,  and  Brian  Wynne.  “Risk  Communication:  Paradigm  and  Paradox.”  Risk  Analysis  9,  no.  2  (1989):  141–45.  

Pagno,  Thomas  C.,  Forian  Pappenberger,  Andrew  W.  Wood,  Maria-­‐Helena  Ramos,  Anders  Persson,  and  Brett  Anderson.  “Automation  and  Human  Expertise  in  Operational  River  Forecasting.”  WIREs  Water  3  (October  2016):  692–705.  

Palmer,  T.  N.,  and  S.  Tibaldi.  “On  the  Prediction  of  Forecast  Skill.”  Monthly  Weather  Review  116  (December  1988):  2453–80.  

Petersen,  Katrina.  “Producing  Space,  Tracing  Authority:  Mapping  the  2007  San  Diego  Wildfire.”  Sociological  Review  62,  no.  S1  (2014):  91–113.  

Phillips,  AL.  “Crowdsourcing  Gender  Equity.”  American  Scientist  99,  no.  6  (2011):  463–64.  Polanyi,  M.  “Tacit  Knowing.”  In  The  Tacit  Dimension,  1–27.  Chicago:  University  Of  Chicago  

Press,  1966.  Popper,  Karl.  “Conjectures  and  Refutations.”  In  Conjectures  and  Refutations:  The  Growth  of  

Scientific  Knowledge,  1–45.  New  York:  Routledge,  1963.  Porter,  T.  M.  Trust  in  Numbers:  The  Pursuit  of  Objectivity  in  Science  and  Public  Life.  

Princeton:  Princeton  University  Press,  1995.  Priyadharshini,  Esther.  “Coming  Unstuck:  Thinking  Otherwise  about  ‘Studying  Up.’”  

Anthropology  &  Education  Quarterly  34,  no.  4  (2003):  420–37.  Ramage,  C.S.  “Prognosis  for  Weather  Forecasting.”  Bulletin  of  the  American  Meteorological  

Society  57,  no.  1  (January  1976):  4–10.  Ravetz,  Jerome.  Scientific  Knowledge  and  Its  Social  Problems.  Oxford:  Clarendon  Press,  1971.  Reed,  Richard  J.  “Bjerknes  Memorial  Lecture:  The  Development  and  Status  of  Modern  

Weather  Prediction.”  Bulletin  of  the  American  Meteorological  Society  58,  no.  5  (May  1977):  390–99.  

Reinhardt,  Martin.  “Spirit  Food.”  In  Indigenous  Innovation:  Universalities  and  Peculiarities,  edited  by  Elizabeth  Sumida  Huaman  and  Bharath  Sriraman,  81–105.  Rotterdam,  Netherlands:  Sense  Publishers,  2015.  

186

Ripberger,  Joseph  T.,  Carol  L.  Silva,  Hank  C.  Jenkins-­‐Smith,  Deven  E.  Carlson,  Mark  James,  and  Kerry  G.  Herron.  “False  Alarms  and  Missed  Events:  The  Impact  and  Origins  of  Perceived  Inaccuracy  in  Tornado  Warning  Systems.”  Risk  Analysis  35,  no.  1  (2015):  44–56.  

Robberson,  Julie.  Norman  Medical  Center  Interviews.  Personal  Interview,  July  23,  2013.  Rochman,  Chelsea  M.,  Eunha  Hoh,  Tomofumi  Kurobe,  and  Swee  J.  Teh.  “Ingested  Plastic  

Transfers  Hazardous  Chemicals  to  Fish  and  Induces  Hepatic  Stress.”  Scientific  Reports  3  (2013):  1–7.  

Rose,  G.  “Situating  Knowledges:  Positionality,  Reflexivities  and  Other  Tactics.”  Progress  in  Human  Geography  21,  no.  3  (September  1997):  305–320.  doi:10.1191/030913297673302122.  

Rose,  Nikolas.  “Governing  by  Numbers:  Figuring  out  Democracy.”  Accounting  Organizations  and  Society  16,  no.  7  (1991):  673–92.  

Rosenfeld,  Jeff.  “Do  We  Need  the  National  Weather  Service?”  Scientific  American,  2000,  28–31.  

Russell,  B.  “Beyond  Activism/Academia:  Militant  Research  and  the  Radical  Climate  and  Climate  Justice  Movement.”  Area  47  (2015):  222–29.  

Russell,  B.,  A.  Pusey,  and  A.  Chatterton.  “What  Can  an  Assemblage  Do?  Seven  Propositions  for  a  More  Strategic  and  Politicized  Assemblage  Thinking.”  City  15,  no.  5  (2011):  577–83.  

Samenow,  Jason.  “Senate  Bill  Proposes  Centralizing  Weather  Service  Forecasting  in  6  Regional  Offices.”  The  Washington  Post.  June  16,  2015,  sec.  The  Capital  Weather  Gang.  https://www.washingtonpost.com/news/capital-­‐weather-­‐gang/wp/2015/06/16/senate-­‐bill-­‐proposes-­‐centralizing-­‐weather-­‐service-­‐forecasting-­‐into-­‐6-­‐regional-­‐offices/.  

Sanders,  Frederick.  “Skill  in  Forecasting  Daily  Temperature  and  Precipitation:  Some  Experimental  Results.”  Bulletin  of  the  American  Meteorological  Society  54,  no.  11  (1973):  1171–79.  

———.  “Trends  in  Skill  of  Daily  Forecasts  of  Temperature  and  Precipitation,  1966-­‐78.”  Bulletin  of  the  American  Meteorological  Society  60,  no.  7  (July  1979):  763–69.  

Saxton,  Devra,  Phil  Brown,  Samarys  Seguinot-­‐Medina,  Lorraine  Eckstein,  David  O.  Carpenter,  Pamela  Miller,  and  Vi  Waghiyi.  “Environmental  Health  and  Justice  and  the  Right  to  Research:  Institutional  Review  Board  Denials  of  Community-­‐Based  Chemical  Biomonitoring  of  Breast  Milk.”  Environmental  Health  14,  no.  90  (2015):  1–13.  

Schaefer,  Joseph  T.  “Severe  Thunderstorm  Forecasting:  A  Historical  Perspective.”  Weather  and  Forecasting  1  (December  1986):  164–89.  

Schrag,  Zachary.  Ethical  Imperialism:  Institutional  Review  Boards  and  the  Social  Sciences,  1965-­‐2009.  Baltimore,  MD:  Johns  Hopkins  University  Press,  2010.  

Schrank,  W.  E.,  and  N.  Roy.  “The  Newfoundland  Fishery  and  Economy  Twenty  Years  after  the  Northern  Cod  Moratorium.”  Marine  Resource  Economics  28,  no.  4  (2013):  397–413.  

Schumacher,  R.  S.,  Lindsey,  D.  T.,  Schumacher,  A.  B.,  Braun,  J.,  Miller,  S.  D,  and  Demuth,  J.  L.  “Multidisciplinary  Analysis  of  an  Unusual  Tornado:  Meteorology,  Climatology,  and  the  Communication  and  Interpretation  of  Warnings.”  Weather  and  Forecasting  25  (2010):  1412–1429.  

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———.  “Multidisciplinary  Analysis  of  an  Unusual  Tornado:  Meteorology,  Climatology,  and  the  Communication  and  Interpretation  of  Warnings.”  Weather  and  Forecasting  25  (2010):  1412–29.  

Schuman,  Frederick  G.  “History  of  Numerical  Weather  Predication  Ad  the  National  Meteorological  Center.”  Weather  and  Forecasting  4  (September  1989):  286–96.  

Schuurbiers,  Daan.  “What  Happens  If  the  Lab  Does  Not  Stay  in  the  Lab?:  Applying  Midstream  Modulation  to  Enhance  Reflection  in  the  Laboratory.”  Science  and  Engineering  Ethics  17,  no.  4  (2011):  769–788.  

Select  Committee  on  the  National  Weather  Service.  “Committee  on  National  Weather  Service  Modernization.”  Washington,  D.C.:  National  Academy  of  Sciences,  1980.  

Sen.  Rick  Santorum  [R-­‐PA].  National  Weather  Service  Duties  Act  of  2005,  2005.  https://www.congress.gov/bill/109th-­‐congress/senate-­‐bill/00786.  

Shapin,  Steven,  and  Simon  Schaffer.  Leviathan  and  the  Air-­‐Pump:  Hobbes,  Boyle  and  the  Experimental  Life.  Princeton:  Princeton  University  Press,  1985.  

Shore,  Nancy,  Elaine  Drew,  Ruta  Brazauskas,  and  Sarena  D.  Seifer.  “Relationships  Between  Community-­‐Based  Processes  for  Research  Ethics  Review  and  Institution-­‐Based  IRBs:  A  National  Study.”  Journal  of  Empirical  Research  on  Human  Research  Ethics  6,  no.  2  (June  2011):  13–21.  

Simmons,  Kevin  M.,  and  Daniel  Sutter.  “False  Alarms,  Tornado  Warnings,  and  Tornado  Casualties.”  Weather,  Climate,  and  Society  1  (2009):  38–53.  

Sismondo,  Sergio.  “Science  and  Technology  Studies  and  an  Engaged  Program.”  In  The  Handbook  of  Science  and  Technology  Studies,  edited  by  Edward  J.  Hacket,  Olga  Amsterdamska,  Michael  E.  lynch,  and  Judy  Wajcman,  13–32.  Boston,  MA:  MIT  Press,  2010.  

Situaciones,  Colectivo.  “On  the  Researcher-­‐Militant.”  Translated  by  Sebastian  Touza.  Uropean  Institute  for  Progressive  Cultural  Policies,  2003.  http://eipcp.net/transversal/0406/colectivosituaciones/en.  

“Skill.”  AMS  Glossary  of  Meteorology.  American  Meteorological  Society,  n.d.  http://glossary.ametsoc.org/wiki/Skill.  

Snellman.  “Operational  Forecasting  Using  Automated  Guidance.”  Bulletin  of  the  American  Meteorological  Society  58,  no.  10  (1977):  1036–44.  

Snellman,  Leonard  W.  “Impact  of  AFOS  on  Operational  Forecasting.”  In  Ninth  Conference  on  Weather  Forecasting  and  Analysis,  13–16.  Seattle,  Washington,  1982.  

———.  “Man-­‐Machine  Mix  in  Applied  Weather  Forecasting  in  the  1970’s.”  Technical  Memorandum.  Salt  Lake  City,  Utah:  Weather  Bureau,  August  1969.  

Spann,  James,  Bill  Murray,  Nate  Johnson,  J.B.  Elliot,  and  Brian  Peters.  Violent  Tornado  in  Moore,  OK.  WeatherBrains,  n.d.  

Star,  Susan  Leigh,  and  James  R  Griesemer.  “Institutional  Ecology,  ‘Translations’  and  Boundary  Objects:  Amateurs  and  Professionals  in  Berkeley’s  Museum  of  Vertebrate  Zoology,  1907-­‐39.”  Social  Studies  of  Science  19,  no.  3  (1989):  387–420.  

Stark,  Laura.  Behind  Closed  Doors:  IRBS  and  the  Making  of  Ethical  Research.  Chicago,  IL:  University  Of  Chicago  Press,  2011.  

Stenhouse,  Niel,  Edward  Maibach,  Sara  Cobb,  Ray  Ban,  Andrea  Bleistein,  Paul  Croft,  Eugene  Bierly,  Keith  Seitter,  Gary  Rasmussen,  and  Anthony  Leiserowitz.  “Meteorologists’  Views  About  Global  Warming:  A  Survey  of  American  Meteorological  Society  

188

Professional  Members.”  Bulletin  of  the  American  Meteorological  Society  95,  no.  7  (2014):  1029–39.  

Stern,  Harvey.  “The  Future  Role  of  Humans  in  the  Weather  Forecasting  Process  –  to  Provide  Input  to  a  System  That  Mechanically  Integrates  Judgmental  (Human)  and  Automated  Predictions?,”  1993.  

Stewart,  Thomas  R.,  William  R.  Moninger,  Janet  Grassia,  Ray  H.  Brady,  and  Frank  H.  Merrem.  “Analysis  of  Expert  Judgment  in  a  Hail  Forecasting  Experiment.”  Weather  and  Forecasting  4  (March  1989):  24–34.  

Stewart,  T.R.,  P.R.  Roebber,  and  L.F.  Bosart.  “The  Importance  of  the  Task  in  Analyzing  Expert  Judgment.”  Organizational  Behavior  and  Human  Decision  Processes  69  (1997):  205–19.  

Stringer,  Ernie.  Action  Research.  New  York:  SAGE  Publications,  Inc,  2013.  STS  Policy  Committee.  “Department  of  Science  and  Technology  in  Society  Graduate  

Program  Rules  and  Procedures.”  Virginia  Tech,  November  2,  2015.  Superstorm  Research  Lab.  “A  Tale  of  Two  Sandys.”  White  Paper.  New  York,  2013.  

https://superstormresearchlab.files.wordpress.com/2013/10/srl-­‐a-­‐tale-­‐of-­‐two-­‐sandys.pdf.  

Swanson-­‐Kagan,  Joanne,  J.  E.  Ten  Hoeve,  III,  Andrea  Bleistein,  J.  Pavlow,  J.  Morrow,  and  C.  Draggon.  “Update  on  the  NWS  Operations  and  Workforce  Analysis.”  Oral  Presentation  presented  at  the  American  Meteorological  Society,  New  Orleans,  January  11,  2016.  https://ams.confex.com/ams/96Annual/webprogram/Paper287551.html.  

Szerszyniski,  Bronislaw.  “Risk  and  Trust:  The  Performative  Dimension.”  Environmental  Values  8  (1999):  239–52.  

Tanaka,  K.,  R.  Takada,  K.  Yamashita,  M.  Mizukawa,  A.  Fukawaka,  and  Y.  Watanuki.  “Accumulation  of  Plastic-­‐Derived  Chemicals  in  Tissues  of  Seabirds  Ingesting  Marine  Plastics.”  Marine  Pollution  Bulletin  69,  no.  1  (2013):  1–2.  

Taylor,  Myfanwy.  “‘Being  Useful’  after  the  Ivory  Tower:  Combining  Research  and  Activism.”  Area  46,  no.  3  (September  2014):  305–12.  

The  Weather  Service  Modernization  Act  of  1992,  1992.  Tierney,  Kathleen.  The  Social  Roots  of  Risk:  Producing  Disasters,  Promoting  Resilience.  

Stanford,  CA:  Stanford  University  Press,  2014.  Traweek,  Sharon.  Beamtimes  and  Lifetimes:  The  World  of  High  Energy  Physics.  Cambridge,  

MA:  Harvard  University  Press,  1988.  Turner,  Stephen.  Explaining  the  Normative.  Cambridge:  Polity  Press,  2010.  U.S.  Department  of  Commerce.  “Evolution  of  the  National  Weather  Service.”  Government  

site.  National  Weather  Service,  September  10,  2014.  http://www.weather.gov/timeline.  

———.  “NOAA  Strategic  Priority:  Building  a  Weather-­‐Ready  Nation.”  Silver  Spring,  Maryland,  December  2011.  

———.  “Program  Development  Plan:  Automation  of  Field  Operations  and  Services.”  Silver  Spring,  Maryland:  National  Oceanic  and  Atmospheric  Administration,  June  1976.  

Usher,  Peter  J.  Communicating  about  Contaminants  in  Country  Food:  The  Experience  in  Aboriginal  Communities.  Ottawa:  Research  Department,  Inuit  Tapirisat  of  Canada,  1995.  

189

Verbout,  S.  M.,  H.  E.  Brooks,  L.  M.  Leslie,  and  D.  M.  Schultz.  “Evolution  of  the  US  Tornado  Database:  1954-­‐2003.”  Weather  and  Forecasting  21,  no.  1  (February  2006):  86–93.  doi:10.1175/waf910.1.  

Wassall,  Robert  B.  “A  Study  of  the  Significance  of  Forecaster  Changes  to  MOS  Guidance.”  National  Weather  Digest,  1977,  27–30.  

Waziyatawin,  and  Michael  Yellow  Bird.  For  Indigenous  Eyes  Only:  A  Decolonization  Handbook.  Edited  by  Waziyatawin  Angela  Wilson.  Native  America  Sereie.  Santa  Fe,  NM:  School  for  Advanced  Research  Press,  2005.  

“Weather  Ready  Nation:  NOAA’s  National  Weather  Service  Strategic  Plan.”  Strategic  Plan.  Department  of  Commerce,  2011.  

Whitnah,  Donald  Robert.  A  History  of  the  United  States  Weather  Bureau.  Champagne,  IL:  University  of  Illinois  Press,  1961.  

Wiedman,  D.  “Native  American  Embodiment  of  the  Chronicities  of  Modernity:  Reservation  Food,  Diabetes,  and  the  Metabolic  Syndrome  among  the  Kiowa,  Comanche,  and  Apache.”  Medical  Anthropology  Quarterly  26,  no.  4  (2012):  595–612.  

Winner,  Langdon.  “Upon  Opening  the  Black  Box  and  Finding  It  Empty:  Social  Constructivism  and  the  Philosophy  of  Technology.”  Science,  Technology  &  Human  Values  18,  no.  3  (1993):  362–78.  

Wood,  Vincent  T.,  and  Robert  A.  Weisman.  “A  Hole  in  the  Weather  Warning  System.”  Bulletin  of  the  American  Meteorological  Society  84,  no.  2  (February  2003):  187–94.  doi:10.1175/BAMS-­‐84-­‐2-­‐187.  

Woodhouse,  Edward,  David  J.  Hess,  Steve  Breyman,  and  Brian  Martin.  “Science  Studies  and  Activism:  Possibilities  and  Problems  for  Reconstructivist  Agendas.”  Social  Studies  of  Science  32,  no.  2  (2002):  297–319.  

Woolgar,  Steve,  C  Coopmans,  and  D  Neyland.  “Does  STS  Mean  Business?”  Organization  16,  no.  1  (2009):  5–30.  

Yusoff,  Kathryn,  and  Jennifer  Gabrys.  “Climate  Change  and  the  Imagination.”  Wiley  Interdisciplinary  Reviews:  Climate  Change  2,  no.  4  (July  1,  2011):  516–34.  doi:10.1002/wcc.117.  

Zuiderent-­‐Jerak,  Teun.  “Editorial  Introduction:  Unpacking  ‘Intervention’  in  Science  and  Technology  Studies.”  Science  as  Culture  16,  no.  3  (2007):  227–35.  

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Appendix   Template  of  Memorandum  of  Understanding  for  Mutual  Aid  Research  in  Disasters  

Superstorm  Research  Lab  &  Disaster  Collaboratory    A  memorandum  of  understanding  is  a  document  designed  to  coordinate  expectations  and  procedures  between  groups.  It  is  useful  when  two  groups  that  have  not  previously  worked  together.  There  are  various  uses  of  a  memorandum,  and  the  specific  purpose  is  determined  by  the  parties  involved:  it  might  be  used  to  indicate  good  will  on  the  part  of  both  parties  or  to  help  them  keep  track  of  what  they've  agreed  on.  The  agreement  can  be  used  to  help  to  clarify  the  relationship  between  two  organizations  and  to  make  clear  which  services  or  responsibilities  each  is  responsible  for.  It  can  also  set  out  clear  decision  making  procedures  and  approaches  to  getting  work  done.  It  might  help  to  supplement  legal  documents  created  with  a  university  or  business  partner,  but  it  is  not  a  legally  binding  contract  itself.331      When  drafting  an  MOU,  keep  in  mind  the  purposes  of  the  agreement.  The  MOU  should  be  detailed  and  comprehensive  enough  that  each  partner  has  a  clear  understanding  of  the  collaboration  or  partnerships,  their  role  in  it,  what  is  expected  of  them,  and  what  they  can  expect  from  the  rest  of  the  group.  It  should  also  be  broad  and  simple  enough  to  support  a  nimble,  adaptable  collaborative  effort.  That  is,  the  MOU  should  support  the  work  of  the  collaboration,  not  get  in  the  way.  Most  importantly,  the  MOU  is  a  framework  for  ethics;  the  research  ethics  supported  by  academic  Institutional  Review  Boards  (IRBs)  do  not  cover  many  types  of  challenges  encountered  in  innovative  research,  collaborations,  and  unique  populations  or  situations  (see,  for  example,  Denzin  and  Giardina  2007).  Thus,  it  is  up  the  collaborators  to  define  the  terms,  scope,  and  elements  of  the  work.      The  MOU  provided  here  is  a  template  to  help  you  start  your  discussions.  It  is  designed  to  be  a  resource  for  a  mutually  beneficial  researcher-­‐community  or  academic-­‐activist  partnerships.  It  covers  a  number  of  different  types  of  collaborations  and  partnerships,  as  well  as  various  issues  that  might  need  discussion;  it  is  neither  a  mandatory  nor  comprehensive  list  of  ingredients  but  is  meant  as  a  starting  point  for  discussion.  In  fact,  some  items  in  the  template  are  contradictory  to  others,  anticipating  a  range  of  possible  frameworks  and  philosophies  of  collaboration.  At  a  macro  level,  it  is  modeled  after  Tribal  Research  Ethic  Codes,  community-­‐based  participatory  research  (CBPR),  and  participatory  action  research  (PAR)  methods.  Language  and  ideas  were  sampled  from  the  following  sources:  

• The  Canadian  Aboriginal  AIDS  Network  MOU  on  Principles  of  Research  Collaboration  

• The  Memorandum  of  Understanding  for  the  Community  Organizing  Part  of  Community  Action  Against  Asthma  (Between:  University  of  Michigan  School  of  Public  Health,  Detroiters’s  Working  for  Environmental  Justice  (DWEJ),  the  Detroit  

331 Note that this MOU should not be used as a substitute for a legal document. It is not intended for this purpose; however, the principles herein may offer a useful supplement to the expectation, practice, and ethical considerations of the collaboration.

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Hispanic  Development  Corporation  (DHDC)  and  Warren  Conner  Development  Coalition  (WCDC)).    

• Healthy  African  American  Families  Community  Participatory  Research  Collaboration  Agreement  

• Language  Revitalization  In  Vancouver  Island  Salish  Communities  project      (http://www.docstoc.com/docs/135504197/Memorandum-­‐of-­‐Understanding)  

• Collaboration  Toolkit:  Creating  an  MOU,  from  Colorado  Collaboration  Award  (http://www.growourregion.ca/images/file/Collaboration%20Toolkit%20-­‐Creating%20an%20MOU.pdf)  

• Indigenous  Research  Protection  Act  by  Indigenous  Peoples  Council  on  Biocolonialism  

• Model  Tribal  Research  Code  by  the  American  Indian  Law  Center      For  questions,  information,  or  to  provide  input,  contact  Max  Liboiron  at  mliboiron@mun.ca.  

 Version  02,  July  2014.    

This  work  is  licensed  under  a  Creative  Commons  Attribution-­‐NonCommercial-­‐ShareAlike  4.0  International  License.  

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 Memorandum  of  Understanding  

This  Memorandum  of  Understanding  made  on  and  effective  from  the  _________  day  of  __________________,  ___  

 is  created  between  [community  group]  

and  [researcher  or  research  institution/second  group]  

 I.  Background  

• Describe  the  parties,  including  who  is  part  of  them  (who  this  MOU  covers)  • Liaison  Officials:  First  and  Second  Points  of  Contact  for  each  organization  and  their  

contact  information  and/or  full  list  of  participants  with  contact  people  specified  (specified  contact  people  eases  communication  efforts  during  project  work)  

• Describe  the  project    II.  Shared  Goals  and  Objectives  The  Parties  have  entered  into  a  collaborative  project  to  work  towards  the  following  goals  and  objectives:  

• The  project  seeks  to  enhance  the  community’s  welfare  through  increasing  capacity  for  the  community  to  address  its  own  issues.  

• The  project  will  be  designed  to  increase  community  knowledge  of  the  issue.  • The  project  will  be  designed  in  ways  that  enhance  research  capacity  or  other  

information  gathering  capacities  of  the  community  participants  in  the  process.  • The  research  objectives,  questions,  and/or  methods  must  not  only  reflect  academic  

interests  but  strive  to  ensure  that  the  research  is  also  relevant,  beneficial,  and  valuable  to  local  communities.  

• Community  and  academic  participants  will  be  involved  in  all  project  phases,  including  planning,  implementation,  research,  evaluation,  analysis,  interpretation,  and  dissemination;  the  burden  under  this  code  is  on  the  researcher  to  show  that  tribal,  community,  or  individual  input  would  be  inappropriate  rather  than  the  reverse.    

• All  participating  members  (academic  and  community  participants)  are  acknowledged  as  having  expertise  and  commitment  that  is  relevant  to  the  scope  of  the  project.  

• Interested  members  of  the  community  and  community  agencies  will  be  provided  opportunities  to  participate  meaningfully  in  the  research  process,  where  the  mode  and  scope  of  participation  is  proposed  and  accepted  by  both  groups.  

• Project  membership  is  considered  to  be  open  or  inclusive  of  those  who  wish  to  join  and  are  willing  to  participate  actively,  rather  than  closed  or  exclusive  in  membership.  

• Community  participants  and  academic  participants  will  be  partnered  with  each  other  on  all/certain  specific  tasks  as  a  way  to  work  together  on  analytic  issues,  including  interpretation,  synthesis,  and  verification  of  conclusions,  gathering  data  and  other  aspects  of  methodology.  

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• For  a  worksheet  on  “Indicators  for  Promoting  Equitable  Collaboration,”  see  Access  Alliance,  2011.    

 III.  Process    Roles,  duties,  and  responsibilities  of  each  organization:  

Meetings  • Parties  will  meet  a  minimum  of  [number  of  times  per  time  period].  • [The  PI  or  project  coordinator  or  rotating  member  drawn  from  either  party]  will  

provide  each  member  of  the  research  team  with  notes  of  meetings,  including  decisions  made,  within  [a  reasonable  time  frame].  

 Project  Design  • Outline  roles  of  each  party  and/or  roles  of  individuals  or  groups  within  those  

parties.  • Parties  will  seek  to  combine  traditional  and  innovative  forms  of  research.  • The  project  will  periodically  assess  the  experience  of  participating  for  community  

and  academic  participants  and  attend  to  their  concerns.    

Data  Parties  should  agree  on  what  counts  as  data  in  this  partnership:  photographs,  stories,  field  notes,  surveys,  interviews,  artifacts,  local  knowledge,  etc.      

Informed  Consent  • The  (purpose  of)  research  project  will  be  explained  to  all  stakeholders  

(participants  and  community  members)  in  a  language  that  is  appropriate  to  the  community.  This  is  part  of  a  wider  community  consent.    

• It  is  requested  that  each  participating  community  partner  have  at  least  one  participating  member  (i.e.,  the  Council  representative)  complete  a  certification  of  training  for  human  subjects  research  through  the  academic  partner’s  institution,  whether  it  is  an  Internal  Review  Board  (IRB),  journalism  ethics,  etc.  This  is  not  to  give  academic  ethics  priority,  but  to  ensure  that  all  parties  are  familiar  with  the  terms  and  processes  academics  are  minimally  accountable  for  (for  integrating  community  and  institutional  ethics  more  formally,  see  Khanlou  and  Peter  2005).      

• The  research  team  will  explain  potential  risks  and  benefits  in  a  manner  that  is  appropriate  to  the  community.  This  includes  not  only  risks  of  the  research  to  individual  participants  but  also  to  the  wider  community  and  third  parties  (see  Underkuffler  2007).  

• Since  researchers  cannot  always  anticipate  risks  of  research  to  the  wider  community,  particularly  if  they  are  not  familiar  with  the  community,  at  least  one  member  of  the  research  subject  population  must  be  involved  to  speak  to  the  risks  of  particular  types  of  research  done  in  that  area.    

• The  informed  consent  of  individual  community  members  must  be  secured  in  writing  before  they  participate  in  research  or  recordings,  including  any  restrictions  the  individual  community  members  might  wish  to  attach  to  the  

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use  of  this  information  or  recordings.    Written  informed  consent  is  evidenced  by  the  signature  of  the  individual  community  member  on  the  Participant  Consent  Form.  In  cases  where  written  forms  of  consent  are  not  appropriate,  another  method  of  acknowledging  consent  with  clear  indications  of  when  it  has  been  obtained  will  decided  on  by  both  parties.            

Confidentiality  Statement  • Unless  the  respondent  waives  confidentiality  for  specified  uses,  all  researchers,  both  

academic  and  community,  shall  hold  as  privileged  and  confidential  all  information  that  might  identify  a  respondent  with  his  or  her  responses.  We  shall  also  not  disclose  or  use  the  names  of  respondents  for  non-­‐research  purposes  unless  the  respondent  grants  us  permission  to  do  so.  

• All  data  will  be  used  in  a  form  that  will  make  it  impossible  to  determine  the  identity  of  the  individual  responses.  That  is,  responses  will  not  be  integrated,  analyzed,  or  reported  in  any  way  in  which  the  confidentiality  of  the  responses  is  not  absolutely  guaranteed.    

Data  Ownership  Parties  should  discuss  what  it  means  to  own,  hold,  or  steward  data  and  the  responsibilities  this  entails.  • Originals  of  all  audio/visual  recordings  (in  digital  and/or  analog  formats)  and  copies  

of  all  notes,  transcripts,  photographs,  and  other  records  of  the  research  will  be  kept  by  [List  parties].          

• [List  parties]  will  retain  a  copy  of  the  full  data  file,  de-­‐identified  appropriately.  • Any  site  owning  data,  or  participating  in  collecting  data  for  the  project,  must  review  

its  participation  and  role  through  their  internal  IRB  and/or  other  indication  of  ethical  protocols  decided  by  group  members.    

• All  participating  sites/partners  will  receive  a  summary  of  the  data  even  if  their  involvement  is  minimal  and  they  are  not  entitled  to  the  full  data.  

• The  parties  will  ensure  that  a  final,  permanent  repository  for  the  research  materials,  to  be  created  by  the  researchers,  will  be  utilized.    Additionally,  the  researchers  will  make  as  a  condition  of  the  deposition  that  the  repository  will  provide  access  to  community  members.    Further,  the  repository  will  adhere  to  any  confidentiality  or  use  restrictions  made  by  the  individual  community  members.      

• Parties  will  outline  rules  for  gaining  or  granting  access  to  the  data  by  third  parties  not  listed  in  this  MOU.            

Community  and  Academic  Validity  • During  the  life  of  the  project,  submitted  research  papers  and  abstracts  for  

presentations  will  be  circulated  to  all  parties  via  lead  participants  at  least  [timeframe]  and  preferably  [timeframe]  prior  to  their  submission  for  review  and  comment.  There  will  be  [timeframe]  for  comments  to  the  lead  author.    

• Each  project  deliverable  will  have  one  or  two  lead  individuals  to  permit  accountability,  preferably  a  representative  from  each  party.  

• It  is  expected  that  the  first  or  senior  author  of  each  project  will  review  comments  from  partners,  discuss  major  differences  of  opinion  with  the  partners  involved,  and  

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circulate  the  final  version  to  partners.  If  substantial  disagreements  over  interpretation  remain,  then  the  lead  author  (first  and/or  senior)  will  include  a  statement  in  the  discussion  section,  clarifying  the  nature  of  the  disagreement.  

• -­‐or-­‐  If  there  are  significant  disagreements  over  interpretation,  community  members  can  veto  the  publication  of  certain  elements  or  all  parties  must  reach  a  consensus  before  such  elements  are  published.  This  may  also  be  the  case  if  some  information  ought  to  not  be  in  the  public  domain  according  to  community  members  or  non-­‐academic  partners,  such  as  but  not  limited  to  sacred  knowledge.    

• -­‐or-­‐  Team  member(s)  or  a  partner  may  choose  to  include  a  disclaimer  if  they  do  not  agree  with  the  content  or  views  presented  in  a  publication.  

• Products  for  community  release  and  presentation  will  be  circulated  for  comments  to  community  and  academic  partners,  providing  a  [time  frame]  turn  around  time.  These  comments  can  be  held  in  a  public  forum  such  as  a  community  meeting,  and/or  in  writing.    

• Given  that  all  members  of  the  research  team  will  be  provided  the  opportunity  to  review  and  comment  on  findings  prior  to  publication  or  presentation,  any  one  member  of  the  research  team  may  not,  particularly  once  initial  dissemination  has  occurred,  further  analyze,  publish,  or  present  findings  resulting  from  the  above-­‐mentioned  research  project  unless  the  entire  research  team  reaches  a  consensus.  

 Dissemination    

• Communication  strategies  to  present  aggregate  data  to  the  community  at  large  shall  be  described  with  in-­‐progress  updates  where  appropriate.  

• Dissemination  of  the  research  results  will  be  the  responsibility  of  all  project  participants,  and  academic  and  community  partners  will  have  opportunities  for  presentations  and  publications.  

• Research  projects  will  produce,  interpret,  and  disseminate  the  findings  to  community  members  in  clear  language  respectful  to  the  community  and  in  ways  that  will  be  useful  for  developing  plans  that  will  benefit  the  community.    

• Research  shall  be  disseminated  for  public  benefit,  either  freely  (including  open  access)  or  at  nominal  charge  to  cover  distribution/processing  fees.  

• The  researchers  will  ensure  that  two  copies  of  all  publications,  conference  papers,  and  other  educational  and  scholarly  materials  produced  in  the  course  of  the  project  be  deposited  with  the  [community  group,  institution,  etc].            

• In  addition  to  academic  papers,  accessible  formats  of  research  findings  will  be  produced  and  distributed,  such  as  webinars,  public  presentations,  videos,  websites,  leaflets,  white  papers,  manuals,  blog  posts,  etc.  

• All  academic  publications  should  be  open  access.      

Publication    These  guidelines  can  be  used  for  traditional  academic  publications  as  well  as  other  formats  for  disseminating  research  findings.    

• Due  to  the  fundamentally  collaborative  nature  of  this  partnership,  party  affiliations,  rather  than  author  names  will  be  used  to  designate  authorship  of  publications.    

• -­‐or-­‐  Due  to  the  fundamentally  collaborative  nature  of  this  partnership,  (1)  All  participants  who  made  this  research  possible  through  conception,  design,  analysis,  

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collection,  provision  or  interpretation  of  data  will  be  listed  as  an  author,  even  when  these  contributions  do  not  include  writing;  and  (2)  authors  must  approve  the  final  draft  and  be  able  to  defend  the  published  work.    

• -­‐or-­‐  Criteria  outlined  by  Huth  (1986)  will  be  used  as  guidelines  for  authorship  of  publication  (both  academic  and  non-­‐academic)  based  on  the  findings  of  the  research.  The  criteria  recommend  that:  (1)  all  authors  must  make  a  substantial  contribution  to  the  conception,  design,  analysis,  or  interpretation  of  data,  where  “substantial”  is  defined  by  parties  ahead  of  time  and  updated  as  needed;  (2)  authors  must  be  involved  in  writing  and  revising  the  manuscript  for  intellectual  content;  and  (3)  authors  must  approve  the  final  draft  and  be  able  to  defend  the  published  work.  Those  who  have  made  other  contributions  to  the  work  (e.g.  data  collection  without  interpretation,  etc.)  or  only  parts  of  the  above  criteria  should  be  credited  in  the  acknowledgements,  but  not  receive  authorship.    

• -­‐and/or-­‐  the  publication  contains  a  section  outlining  what  each  author  contributed,  acknowledging  that  “authorship”  can  include  the  collection  and  interpretation  of  information  as  well  as  actual  writing  up  of  results.  

• The  explicit  permission  of  an  individual  or  organization  must  be  sought  prior  to  acknowledging  their  contribution  in  a  paper  or  presentation.  

• Parties  should  agree  on  publication  venues  together.    IV.  Communication  

• Include  any  standard  or  shared  terminology,  including  consistent  ways  that  partners  are  identified  in  written  and  verbal  communication.  

• Consider  and  decide  on  processes  for  reaching  out  to  –  or  receiving  requests  from  –  third  parties,  such  as  the  press,  other  groups  and  institutions,  interested  members  of  the  public,  etc.    

• Consider  and  decide  on  general  communications  policies  (social  media  policies,  communications  calendar,  branding,  graphic  standards,  etc.  as  applicable)  

• Include  any  information  flow  practices  that  will  help  guide  how  data,  ideas,  and  needs  are  shared  between  groups.  

 V.  Resource  Allocation  Payment,  fees,  and  funding  Include  budget,  if  appropriate.  Note  that  when  money  exchanges  hand,  a  contract,  rather  than  a  memorandum  of  understanding,  is  likely  more  appropriate.  For  information  on  when  to  use  a  binding  contract  vs  a  MOU,  see:  http://ctb.ku.edu/en//tablecontents/sub_section_main_1873.htm  

• Both  parties  shall  contribute  in-­‐  kind,  including  the  following  funding,  labor,  equipment,  and  space  [list]  

• [List  partner]  will  handle  all  financial  transactions  on  behalf  of  the  collaboration.  The  following  [reports,  procedures,  or  financial  controls]  are  required  of  [the  partner]  

• Expenses  inclusive  of  [list  types]  will  be  handled  by  [outline  procedure  &  responsibilities]  

Also  consider:  

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• Gift  acceptance  policies:  these  should  describe  how  gifts  are  accepted,  recorded,  and  acknowledged.  In  addition,  the  MOU  should  describe  the  circumstances  under  which  a  gift  would  be  declined.  

• Policies  around  sharing  fundraising  information  externally  and  among  partners,  and  responsibility  of  fundraising  

• Payment.  Which  partners  or  individuals  will  be  paid  and  from  what  source?    VI.  Decision  Making  Processes  

• Things  to  specify:  • Whether  the  collaboration  uses  a  consensus  model,  majority  vote,  or  another  system  

to  reach  decisions.  • What  constitutes  a  full  group  meeting  or  quorum  (minimum  number  of  people  

required),  and  what  types  of  discussions  or  decisions  may  or  may  not  take  place  without  the  full  group/quorum.  

• How  partners  will  be  informed  in  advance  about  decision-­‐making  discussions  &  what  alternative  voting  systems  may  be  used  (voting  via  email,  sending  a  proxy  to  a  meeting,  etc)  

VII.  Risk  • The  MOU  should  address  key  areas  of  risk  for  the  collaboration.  Partners  may  be  

expected  to  maintain  certain  types  or  levels  of  insurance  coverage,  conduct  background  checks  on  employees  and  volunteers,  maintain  security  of  electronic  data,  etc.  

• Since  researchers  cannot  always  anticipate  risks  of  research  to  the  wider  community,  particularly  if  they  are  not  familiar  with  the  community,  at  least  one  member  of  the  research  subject  population  must  be  involved  to  speak  to  the  risks  of  particular  types  of  research  done  in  that  area.    

 VIII.  Terms  of  Agreement  

• This  agreement  may  be  amended  at  any  time  by  signature  approval  of  the  parties’  signatories  or  their  respective  designees.  

• The  term  of  this  Memorandum  of  Understanding  is  from  ___________________  to  ___________________  and  may  be  renewed.    The  Parties  will  review  this  agreement  [annually/timeframe].  

 IX.  Termination    

• In  case  of  a  dispute  arising  from  the  implementation  of  this  Memorandum  of  Understanding,  the  Parties  shall  exhaust  alternative  dispute  resolution  models,  such  as  negotiation  and  mediation,  before  employing  other  forms  of  dispute  resolution,  such  as  arbitration  or  adjudication.    Parties  shall  act  in  good  faith  to  resolve  the  dispute.          

• Any  Party  may  withdraw  at  any  time  from  this  MOU  by  transmitting  a  signed  statement  to  that  effect  to  the  other  Parties.  This  MOU  and  the  partnership  created  thereby  will  be  considered  terminated  thirty  (30)  days  from  the  date  the  non-­‐withdrawing  Party  receives  the  notice  of  withdrawal  from  the  withdrawing  Party.  

 X.  Execution  and  Approval  

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• The  persons  executing  this  MOU  on  behalf  of  their  respective  entities  hereby  represent  and  warrant  that  they  have  the  right,  power,  legal  capacity,  and  appropriate  authority  to  enter  into  this  MOU  on  behalf  of  the  entity  for  which  they  sign.  

• Signatures  _________________  • Date  _________________  

     Version  02,  July  2014.    This  work  is  licensed  under  a  Creative  Commons  Attribution-­‐NonCommercial-­‐ShareAlike  4.0  International  License.  

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Works  Cited  and  other  resources    Access  Alliance  Multicultural  Health  and  Community  Services.  Community-­‐Based  Research  Toolkit:  Resources  and  Tools  for  Doing  Research  with  Community  for  Social  Change.  Toronto:  Access  Alliance  Multicultural  Health  and  Community  Services,  2011.  http://accessalliance.ca/sites/accessalliance/files/CBR_Toolkit_Jan2012.pdf.  A  superb  and  comprehensive  source  of  tools,  templates  and  resources  compiled  and  developed  by  the  Community  Based  Research  team  at  Access  Alliance  based  on  half  a  decade  of  implementing  Community  Based  Research  projects.      Denzin,  Norman  K.,  and  Michael  D.  Giardina.  Ethical  Futures  in  Qualitative  Research:  Decolonizing  the  Politics  of  Knowledge.  Left  Coast  Pr,  2007.    Huth,  E.  (1985).  Guidelines  on  authorship  of  medical  papers.  American  College  of  Physicians.  Annals  of  Medicine,  104,  269-­‐274.  In  the  belief  that  authors  and  potential  authors  may  be  helped  by  explicit  statements  of  justification  for  authorship,  the  following  guidelines  are  offered  for  research  papers,  case-­‐series  analyses,  case  reports,  review  articles,  and  editorials.  These  guidelines  are  based  on  statements  issued  by  the  International  Committee  of  Medical  Journal  Editors  (ICMJE).    Khanlou,  Nazilla,  and  Elizabeth  Peter.  “Participatory  Action  Research:  Considerations  for  Ethical  Review.”  Social  Science  &  Medicine  60,  no.  10  (2005):  2333–40.  PAR  researchers  and  members  of  Research  Ethics  Boards  could  benefit  from  an  increased  understanding  of  the  array  of  ethical  concerns  that  can  arise.  We  discuss  these  concerns  in  light  of  commonly  held  ethical  requirements  for  clinical  research  (social  or  scientific  value,  scientific  validity,  fair  subject/participant  selection,  favourable  risk–benefit  ratio,  independent  review,  informed  consent,  and  respect  for  potential  and  enrolled  participants)  and  refer  to  guidelines  specifically  developed  for  participatory  research  in  health  promotion.  We  draw  from  our  community-­‐based  experiences  in  mental  health  promotion  research  with  immigrant  and  culturally  diverse  youth  to  illustrate  the  ethical  advantages  and  challenges  of  applying  a  PAR  approach.  We  conclude  with  process  suggestions  for  Research  Ethics  Boards.      Maiter,  Sarah,  Laura  Simich,  Nora  Jacobson,  and  Julie  Wise.  “Reciprocity  An  Ethic  for  Community-­‐Based  Participatory.”  Action  Research  6,  no.  3  (September  1,  2008):  305–25.  In  this  article  we  suggest  that  the  notion  of  reciprocity  —  defined  as  an  ongoing  process  of  exchange  with  the  aim  of  establishing  and  maintaining  equality  between  parties  —  can  provide  a  guide  to  the  ethical  practice  of  CBPAR.  Through  sharing  our  experiences  with  a  CBPAR  project  focused  on  mental  health  services  and  supports  in  several  cultural-­‐linguistic  immigrant  communities  in  Ontario,  Canada,  we  provide  insights  into  our  attempts  at  establishing  reciprocal  relationships  with  community  members  collaborating  in  the  research  study  and  discuss  how  these  relationships  contributed  to  ethical  practice.  We  examine  the  successes  and  challenges  with  specific  attention  to  issues  of  power  and  gain  for  the  researched  community.  We  begin  with  a  discussion  of  the  concept  of  reciprocity,  followed  by  a  description  of  how  it  was  put  into  practice  in  our  project,  and,  finally,  

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conclude  with  suggestions  for  how  an  ethic  of  reciprocity  might  contribute  to  other  CBPAR  projects.    Minkler,  Meredith.  “Ethical  Challenges  for  the  ‘outside’  Researcher  in  Community-­‐Based  Participatory  Research.”  Health  Education  &  Behavior  31,  no.  6  (2004):  684–97.  This  article  explores  several  key  challenges.  These  are  (a)  achieving  a  true  “community-­‐driven”  agenda;  (b)  insider-­‐outsider  tensions;  (c)  real  and  perceived  racism;  (d)  the  limitations  of  “participation”;  and  (e)  issues  involving  the  sharing,  ownership,  and  use  of  findings  for  action.  Case  studies  are  used  in  an  initial  exploration  of  these  topics.  Green  et  al.’s  guidelines  for  appraising  CBPR  projects  then  are  highlighted  as  an  important  tool  for  helping  CBPR  partners  better  address  the  challenging  ethical  issues  often  inherent  in  this approach.