The (network) Structure of Sociology Production James Moody Ohio State University American Sociological Association Philadelphia, August 2005

The (network) Structure of Sociology Production

James MoodyOhio State University

American Sociological AssociationPhiladelphia, August 2005

"Science, carved up into a host of detailed studies that have no link with one another, no longer forms a solid whole."

Durkheim, 1933

Organizations

Historical Sociology

Crime

Health

Stratification

Gender

SocialWelfare

Introduction

Introduction

The extent to which science is “carved up into a host of detailed studies that have no link with one another” is a question of network cohesion.

•A fractured discipline will be dominated by tight clusters based on specific research problems

•An integrated discipline will have strong connections bridging research problems

Introduction

Outline:•Introduction•Theoretical perspectives on scientific development•Network basis of social cohesion•Data Sources•Results

•Where Does Sociology fit? Journal co-citation networks•What do sociologists study? Topic networks•Who produces sociology? Social science collaboration networks

•Discussion

Approaches to Understanding Scientific Coherence

•Normal Science, accumulation & revolution-Science is problem driven & evidence based-Consensus emerges through a competition of ideas against data

-(though lab ethnography repeatedly shows that consensus is often more socially constructed than evidentiary)

-Scientific “Star” systems reward prior success, and stars shape research agendas

•Boundary Specification & Science as a profession-Motivated by prestige & competition for resources

-Competition will lead to both vanquishing and niche filling-Disciplinary identity & coherence become a key issue-Contested fields lead to “chaotic” outcomes (Abbott 2001)

•Invisible Colleges-Informal communities create acceptable scientific standards-Boundaries are defined socially through interaction

•Scientific (social) Movements-Combination of many of these ideas under a social movement frame-Coherence becomes a framing & “grievance” issue used to shape resource allocation

Four relevant networks:

1. Citation networks – a direct trace of scientific recognition & production

2. Topic networks – clusters of scientific products speaking about the same subject

3. Collaboration networks – “invisible communities” of social interaction that produces scientific products

4. Research Communities – People linked through common research topics (merger of 2 & 3)


The field of sociology can thus be thought of as the intersection of multiple networks.

The shape of these networks differs across scales and over time.- Differences between local and global visions of the network shape our perceptions of scientific coherence.

- We tend to perceive coherence in our own specialty fields and incoherence for the entire discipline.

- A globally federated structure, that cannot easily exclude empirical topics, might still be socially coherent if scientific mixing cross-cuts empirical problems.

We can see this structure by examining these 4 networks at large scale and over time.


•Citation Networks•Compiled from the ISI web of science Journal citation tables•Covers 1681 social science journals indexed in 2003

•Topic & Collaboration Networks •Compiled from Sociological Abstracts•281,163 papers published between 1963 and 1999•Contains information on title, abstract, keywords, author(s), tables, journal & citation

Data Sources

Where does sociology fit?

•Perennial debates over the existence of a theoretical core •Rapid growth in the internal diversity of topics sociologist study:

0

5

10

15

20

25

30

35

40

45

50

1950 1960 1970 1980 1990 2000 2010

Number of ASA Sections


•Perennial debates over the existence of a theoretical core to the discipline•Rapid growth in the number of journals relevant to sociologists:

This growth & diversity has been seen as evidence for the ultimate emptiness of sociology as a scientific discipline.

But disciplines are shaped by the connections between ideas, not the number of ideas. That is, we recognize fields by who they speak to as much as by what they speak about.

The clearest empirical trace of this communication is citation.

Disciplines can then be defined as clusters of work that speaks more to each other than to anyone else.



Building co-citation networks

Links in a co-citation network are constructed by measuring how similar each journal is to every other journal.

Similarity is gauged by correlating the pattern of citations received by each journals from every other journal.

AJS ASR AER … JER

J1

J2

J3

J4 . . .JER

# #

# #

# #

# #

0 0

0 0

0 0

0 # # #

0 0

Comparing across columns tells us whether the two journals are recognized by others as similar.


AJS ASR AER … JER

AJS

ASR

AER . . . JER

1.0

1.0

1.0

1.0

High

MedLow

HighLow Low

This create a valued network of ties between two journals. I use a cosine similarity score developed in bibliometrics, selected for those with ties > 0.45 & at sharing at least 2% of their citation volume.

Building co-citation networks

Links in a co-citation network are constructed by measuring how similar each journal is to every other journal.

Similarity is gauged by correlating the pattern of citations received by each journals from every other journal.


Density = 0.197N=152Isolates (not shown): 5

Economics co-citation similarity network

Node size proportional to log(degree)


Political Science co-citation similarity network

Density = 0.160N=69Isolates (not shown): 10

Node size proportional to log(degree)


Sociology co-citation similarity network

Density = 0.140N=69Isolates : 7





•Sociology “fits” at the center of the social sciences. We are not as internally cohesive as Economics or Law, but more so than many (anthropology, allied health fields).

•This represents a tradeoff. We have traded unique dominance of a topic (markets, politics, mind, space, history) for diversity & thus centrality.

•Sociology is interstitial discipline (Abbott, 2004) in at least two-senses:

•There is no content topic we can reasonably exclude•We pull together, and generate, the ideas and topics covered by specialty disciplines.

•This makes us uniquely positioned to provide comprehensive insights on particular empirical questions.

What do sociologists study?

If the citation networks capture the structure of organized disciplines, how do we capture the internal organization of disciplines around research problems?

•Could use paper citation networks (see Hargens 2000), but data are difficult & expensive to obtain for large-scale networks.

•Can examine the network of papers formed by the topics they write about.

•Directly taps scientific content•Purely endogenous creation of topics that allows new topic areas to emerge and old ones to die over time•Data can be extracted from information held in Sociological Abstracts

•Multiple levels:•Coarse grained Focus solely on keywords•Fine grained Use all information available (title, abstract, keywords)

What do sociologists study?A coarse grained view

What about the topic distribution within core sociology journals?

Source: Ryan Light, “Balkanized or Boundless” MA Thesis, OSU. Journals restricted to the 79 identified by Allen (2003) as core sociology journals







What do sociologists study?A fine grained view

Index entries contain title, abstract and keywords that summarize the paper’s content.

•Map paper-to-paper networks for all papers indexed within four 3-year windows from 1970 to 1999.

•Construct a paper – by – word matrix, where the ij cell lists how many times word i is used to describe paper j.

•Word set is stemmed to get at root words•A stop-list is used to minimize inclusion of meaningless English words (“the” “and” “is” etc.)

•Construct a network by linking the most highly correlated papers •Use correlation of 0.40 or better•Ties are treated as valued in the network analyses

•Use network clustering algorithms (Moody 2001) to identify highly cohesive clusters in the resulting networks and contour sociograms to give an intuitive sense of how topics have changed over time


Example: One-step neighborhood of “More information, better jobs?”

A fine grained view


Example: One-step neighborhood of “More information, better jobs?”

A fine grained view






% Clustered: 72.2

% Clustered: 71.1

% Clustered: 72.5

% Clustered: 74.9

Cluster Size DistributionA fine grained view


We can measure the degree of consensus in words used to describe papers with:

C = pi2

Where pi is the proportion of times word i is used


0.11

0.115

0.12

0.125

0.13

1965 1970 1975 1980 1985 1990 1995 2000

Consensus Scores1970 - 1999

C (

x 10

0)


The cluster structure of the topic network for the broader body of sociologically relevant work has remained largely constant:

•About 70% of papers can be clearly assigned to a strong topic cluster over the last 30 years.

•The basic number of clusters has hovered around 110 in all periods, with some no strong indication of growth in number.

•Almost all growth can be accounted for in larger clusters.

•The ~30% of papers that are not embedded in clusters either span multiple well established boundaries or belong to very small niches.


The cluster content of the topic network has evolved slowly:

•Some clearly central specialties have remained prominent over the entire period. This includes larger areas such as:

• Class & Stratification• Race & Ethnicity• Education• Gender (Strongest from 1980s on)• Family (Strongest from the 1980s on)• Crime

As well as clearly distinct, though numerically smaller bodies of research related to

• Suicide• Sociology of Science, Technology & “Reflexive” sociology• Unions


The cluster content of the topic network has evolved slowly:

•The clearest change has been the rapid growth of social research on health.

•Dominated by a very large body of research related to HIV/AIDS•Includes research on all social aspects (discrimination, coping, education, etc) as well as directly health-related work.

•Other areas of relative growth include:•Family topics were most prominent in the 1980s•A strong presence of research on sex & sexuality emerged in the 1980s and 90s

•Relative declines have come in areas such as:• Groups• Interaction• “Radical” studies• Elite studies

Summary: A move away from basic social processes toward studying social problems, with a growing uniqueness of theory & method


A clustered topic structure focused strongly on practical problem solving has a hint of Durkheim’s concern: Is there any integration across these topic clusters?

We shouldn’t jump too quickly to that conclusion:• Topic clusters are formed from papers, and papers typically have well

encapsulated ideas. They have a small “maximum digestible unit”

• Scientific integration is really about how scientists bridge these multiple topics.• If authors write and collaborate across these topics then ideas, methods

and approaches can quickly disseminate as well.

Two aspects to this question:• The structure of the collaboration graph – if this is highly clustered it

would signal potential fragmentation Who produces sociology?

• The dual graphs of topics linked through authors.

Who produces sociology?

Science is typically produced through collaboration, both formally and informally (Crane 1972, Crane & Small 2000, Friedkin 1998).

The best empirical trace of collaboration for large communities of science is coauthorship.

•Misses the less intense collaborations recognized in acknowledgements, discussions, colleagues reading each other’s work

•But should provide the strongest test of a clustering hypothesis, since the set of people we write with should be more like us than the set of people we have lunch with or discuss share work with informally.

•There are differences across subfields in formal collaboration rates, which, if anything, should magnify the extent of observed fragmentation.

Coauthorship Trends in SociologySociological Abstracts and ASR

0

0.15

0.3

0.45

0.6

0.75

1930 1940 1950 1960 1970 1980 1990 2000

Year

Pro

po

rtio

n o

f p

ap

ers

wit

h >

1 a

uth

or

ASR Sociological Abstracts


Distribution of Coauthorship Across JournalsSociological Abstracts, 1963-1999

0

0.2

0.4

0.6

0.8

1

0 100 200 300 400 500 600 700 800 900 1000 1100

Coauthorship Rank

Pro

po

rtio

n o

f p

ap

ers

w.

>1

au

tho

r

AJS

Soc.Forces

ASR

Soc.Theory

Signs

AtcaPolitica

J. Soc.History

J.Am.Statistical A.

J. Health &Soc. Beh.

Child Development



Construct a collaboration network by assigning an edge between any pair of people who coauthored a paper together.

g=745


Example Paths: 3-steps from N. B. Tuma

g=745

Node size = ln(degree)


•High relative probability that a node’s contacts are connected to each other.•Small relative average distance between nodes

C=Large, L is Small = SW Graphs


The simplest summary test for a fragmented network is to measure the extent of clustering in the network. Watts’ work on the “small-world problem” suggests that if the collaboration network is a small world network it might be fractured.

In a highly clustered, ordered network, a single random connection will create a shortcut that lowers L dramatically

Watts demonstrates that Small world properties can occur in graphs with a surprisingly small number of shortcuts


Locally clustered graphs are a good model for coauthorship when there are many authors on a paper.

Paper 1 Paper 2 Paper 3 Paper 4 Paper 5

Newman (2001) finds that coauthorship among natural scientists fits a small world model.


I test this model on the sociology coauthorship network, using all authors from 1963 – 1999.

The sociology network less clustering than would be expected by chance and somewhat longer overall distances.

This suggests that it does not have a small-world structure.

Observed Random

Clustering

Distance

0.194 0.206

9.81 7.57


The network has a broad Core-periphery structure

29,462Bicomponent

38,823

59,866

(68,923)

Component

Unconnected

Structurally Isolated


Largest Bicomponent, g = 29,462

0.04 0.27 0.50 0.73 0.96



Health

General Sociology


Internal Structure of the largest bicomponent

Group 1 Group 2Size 3667 987In-group / out- group ties 3.24 2.86% male 67 52Years in discipline 8.46 4.67Number of co-authored publications 5.32 3.24


Internal Structure of the largest bicomponent

0 10 20 30 40

2-connected

3-connected

4-connected

5+ -connected

29,462

14,672

7,992

5,223


Characteristics of authors by component embeddedness

TotalStructural

Isolate(0)

Unconnected

(1)

Largestcomponent

(2)

Corebicomponent

(3)Percent male 62% 69% 62% 56% 59%Years in the discipline(a) 4.02 2.88 3.44 3.98 7.97Avg. number of authors per paper 2.26 1.0 2.57 2.83 3.78Number of publications 2.17 1.51 1.76 2.02 4.78Number of co-authors 2.05 0.00 1.95 2.48 6.49Year of first publication 1985.85 1984.5 1986.6 1986.6 1986.18N 197074 68932 59,866 38,823 29,462


•Strong specialty effects for ever-coauthoredUnlikely:

History & TheorySociology of KnowledgeRadical / Marxist SociologyFeminist / Gender Studies

Likely:Social psychologyFamilyHealth & MedicineSocial ProblemsSocial Welfare


•Weak specialty effects for network embeddedness•Large number of coauthors increases embeddedness•Large number of people on any given paper decreases embeddedness


Graph Connectivity, Cumulative 1963 - 1999

0

0.1

0.2

0.3

0.4

0.5

0.6

1965 1970 1975 1980 1985 1990 1995 2000

Years (1963 - date)

Pe

rce

nt

% in Giant Component

% of connectedin bicomponent


Network Connectivity: 5-year moving window

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

1975 1980 1985 1990 1995 2000

Year

Per

cen

t

2

2.05

2.1

2.15

2.2

2.25

Co

nn

ecti

vity

Connectivity

Bicomponent

Component


Knowledge Accumulation in Sociology?

Social Science Citation Structure•Economics, Psychology, Business/Management are most cohesive

•The are also “peripheral” in that they speak to a relatively limited set of problems

•Sociology is as cohesive as Political Science, and more cohesive than fields such as Anthropology, Social Work, Education or allied health fields that all have more limited empirical domains

•Sociology has made a tradeoff between internal cohesion and external centrality.


Scientific Topic Network•Big-Picture: A general progression towards problem solving and the specialization of work on theory & methods.

•Fine-grained structure: •A federated topic structure that has remained constant since the 1970s, with about 70% of all papers being clearly clustered.

•Key content areas have remained largely constant•Race, Family, Class, Gender, Science,

•A decrease in focus on general foundation problems•Group structure, community, interaction

•An increase in work on social problems•Health & HIV/AIDS -related topics

•Some evidence for greater homogeneity in topics discussed


Scientific Collaboration Network•The networks is not divided into small research-area based clusters.

•There is no partition that strongly separates scientists, which implies that authors cross topics. •This is good for social cohesion.

•There is some evidence for a division based on research method, with largely quantitative work more likely to be coauthored.


Combined, these models suggest a discipline that is socially integrated globally and strongly cohesive at the local topic level.

Discipline-wide integration will likely only increase as pressures for collaboration push more scientists to work together across topics.

The perception of disintegration will likely continue, however, because most of us only read other areas in the main general journals. But almost all of the topical cohesion is due to the “normal science” occurring in specialty journals.

Documents

The (network) Structure of Sociology Production James Moody Ohio State University American Sociological Association Philadelphia, August 2005