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Referential Treatment in the Age of Cybernetic Cognitive Capitalism1
Not long ago, I began to devote serious thought to the socially constructed phenomenon known
as the academic letter of reference. I’ve been writing such letters for many years, and I presume
you have too. But something feels different. I recently learned of a post-doctoral position in our
field that attracted more than two thousand applicants for a single post. I wrote a letter for a
doctoral student entering an annual competition in which last year’s round awarded ten
fellowships selected from more than a thousand submissions. Another letter went to a
competition where four to six are chosen from a pool that typically exceeds 750. Then, after
reading an Economist special issue on the globalization of higher education (“The World is
Going to University,” [Duncan, 2015]) and traveling southwest of Delhi past billboards for a
private school advertising its excellence in “Educating the Global Child,” I received a new kind
of request. This one asked more invasive questions. Beyond the standard queries about
academic achievement and potential, I was asked to comment on “the candidate’s current salary /
salary on leaving,” and “whether there have been any disciplinary or other formal employment
proceedings against this candidate (including any in process at present / at the time employment
ended.” This was for an academic letter of reference. I was then informed that if I wished, the
institution would treat my recommendation “in the strictest confidence,” but “you are advised
that under the terms of the Data Protection Act, all references are potentially disclosable to the
applicant.” Around the same time, I noticed eerily similar but not identical wording in another
academic reference request, which also asked about the applicant’s conformance with “the
company dress code,” and:
“To your knowledge has the applicant any criminal convictions within the last
seven years or any cases pending before a court?”
This pair of surveillant legalistic injunctions arrived from automated “referee management
systems” at the speed of code, as nearly all reference requests now do in what Marshall
McLuhan (1962, p. 32) foresaw in the “technological brain for the world” that has been creating
and dissolving successive generations of “Gutenberg moments” of geographical learning and
knowledge production (Schuurman, 2013). The reference requests arrived from the
Commonwealth antipodes — one from an elite university in England, the other from a local
1 The author function (Foucault, 1969) associated with this essay corresponds to the legally codified published entity
denoted as ORCID Unauthenticated (2014). Further information on the authorial “disambiguation” procedures of
the Open Researcher and Contributor ID (ORCID) system is available at http://orcid.org, last accessed January 1,
2017. The material presented in this essay corresponds to the plans for aphorisms, deleted passages, references,
notations of meetings, and Nietzschian laundry lists as theorized by Foucault (1969, p. 379). Hence the content is
not to be construed as a “systematic undertaking intended to extend knowledge through a disciplined inquiry or
systematic undertaking”; see UBC BREB (2017). Additionally, pursuant to Articles 2.2-2.6 of the Tri-Council
Policy Statement 2 (TCPS 2), dissemination of this information does not allow identification of specific individuals,
with the exception of existing publicly available information where there is no reasonable expectation of privacy;
there is no intervention staged by the author function; and the content is designed to provide quality assurance and
quality improvement (QA/QI) insights to enhance the conduct of performance reviews and the assessment,
management, and improvement of teaching and learning within the institutions of contemporary cognitive
capitalism. See UBC BREB (2017). I am grateful to Emily Rosenman, Briavel Holcomb, and John S. Adams for
valuable comments, questions, and recommendations on previous versions of this manuscript.
Version 0.5, February 5, 2017
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government in New Zealand. It felt like a latter-day echo of the messages moving at the speed of
electrons slowed only by the nerves of the telegraph operator who relayed signals from
Christchurch to Canterbury in 1863, as Samuel Butler portrayed this “ne plus ultra of perfection
in mechanized development” that would allow “a back country squatter” in New Zealand to hear
market prices and opera performances from London (Dyson, 1997). Butler’s warnings of
accelerating technological evolution re-defining the human condition in the industrial age —
‘Darwin Among the Machines’ — seemed even more relevant to the strange mutations of the
postindustrial era when I learned that my university, a late and somewhat incompetent
competitor in the exploding market for Massive Open Online Courses, now has more than 140
thousand students enrolled in MOOCs. Meanwhile, after scores of emails through a long and
torturous application process to my department’s increasingly competitive Doctoral program, a
candidate who was denied admission wrote one more email — a powerful, eloquent, detailed,
and meticulously-documented missive culminating in a rather explicit threat:
“I am aggravated with your program and the irresponsibility of its process. ...
Fortunately, I enjoy relationships with legal counsel. I write this lengthy mail so
that you are aware ... With a counsel representing for health insurance reasons, I
considered having the letter with the numerous e-mails to establish a case drafted
by him...”
I don’t blame anyone. I expressed strong support for the applicant at every stage, but our
Admissions Committee is forced to balance multiple and contradictory imperatives, each of
which generates a torrent of complex and often fragmentary networked communications about
various stages of review and approval. Central to the structural-functional brilliance of the
neoliberal evolution of educational institutions as agencies of “socialization and allocation”
(Parsons, 1959) is the self-replicating, self-justifying bureaucracy of incessant committee
assessments and endless rounds of rankings to identify the most ‘meritorious’ applicants.2
Everyone in academia is overwhelmed with the endless work of constant reviews and
adjudications; but no individual is ever permitted to make or fulfill promises on a unilateral
basis. My individual respect and enthusiasm for the applicant in this case, when culminating in a
collective institutional rejection, was suddenly transformed into a horrible experience of
deception and betrayal.
All of these things are related. Education — especially the presumptively ‘higher’ education of
postsecondary credentials — is becoming ever more competitive. More students are competing
2 ‘Structural functionalism’ is a theoretical tradition developed by Talcott Parsons (1902-1979) that explains social
relations in terms of the ‘structural imperatives’ that preserve, reproduce, and change dominant bureaucracies,
organizations, and practices. In Parsons’s approach, societies cannot be understood ‘from the actor’s point of view’
of individuals’ choices and decisions, but must be examined in terms of the way powerful institutions adapt to
change, justify their social functions, and help resolve the tensions of varied social actors (see Gregory, 2009).
Parsons’s reworking of Max Weber’s theories of bureaucratic control illuminate the fine-grained details of today’s
universities quite clearly, in part because the theory proved so valuable for the expansion of university-based social
inquiry as the U.S. assumed global imperial leadership after the Second World War. As Graeber (2015, p. 56) puts
it, “Sociologists like Talcott Parsons and Edward Shils were deeply embedded in the Cold War establishment at
Harvard, and the stripped-down version of Weber they created was quickly stripped down further and adopoted by
State Department functionaries and the World Bank as ‘development theory’ and actively promoted as an alternative
to Marxist historical materialism in the battleground states of the Global South.”
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to gain admission, where they find themselves again competing to be allowed to proceed to the
next stage, where they will confront yet another round of competitions. Institutions are also, of
course, competing to recruit the ‘best’ students. Escalating competition, in turn, incentivizes
innovations in strategic manipulation and fraud, necessitating ever more comprehensive systems
of policing and surveillance. This adds new stages of certification and authentication while
increasing the labor invested in an increasingly stressful, tightly-regulated, high-stakes enterprise
with considerable consequences. To be sure, the traditional stereotype of the processes
governing academic admissions, awards, and hiring decisions — relaxed, collegial, heavily
reliant on the fair and impartial scholarly judgment of an institution’s faculty — has never been
the norm. Such processes have always been plagued by a blend of capricious contingency and
deeply-rooted structural biases of class nepotism interacting with ethnoracial and gender
discrimination (Dershowitz and Hanft, 1979). Yet today’s coalescence of cybernetic
informational acceleration and intensified competition takes place amidst a complex and shifting
terrain of mobilizations for gender and ethnoracial equity, inclusion, and cosmopolitan diversity.
Higher education thus now reflects and reproduces a paradoxical dialectical scalar tension. At
the ‘macro’ scale of global and transnational aspirations in what is now being called “cognitive
capitalism” (Moulier-Boutang, 2012; Scott, 2007, 2014), colleges and universities are at the
forefront of an academic entrepreneurialism that privileges news kinds of alliances between
capital and state power (Olds, 2007; Koch, 2014). Such ventures are usually seen from “The
West” in terms of a progressive, postcolonial cosmopolitan diversity — but they often conflict
with the varied and divergent conceptions of cultural history and individual identity, gender
roles, nationalism, and academic freedom in the rising authoritarian growth centers of the “non-
West” (Koch, 2014; Krieger, 2013; Kaminer, 2013). Meanwhile, at the ‘micro’ scale,
information overload, competition, and stress are all intertwined with dramatic increases in the
diagnosis of Attention Deficit / Hyperactivity Disorder (Ellison, 2015) and major initiatives to
deal with a generalized crisis of depression, suicide, and other aspects of student mental health
(PCMH, 2012; UBC, 2016). Another micro-level facet of cosmopolitan cognitive capitalism is
an advanced transformation in the nature of student activism — particularly at the most selective
institutions. Recent student mobilizations have been satirized by the Right, and indeed at first
glance some of the priorities of the most extreme cases — student protests over an email
questioning a blanket prohibition on racially insensitive Halloween costumes at Yale led to
faculty resignations and a new batch of diversity initiatives costing at least $50 million
(Bauerline, 2016) — seem to be disappointing successors to the antiwar and civil rights New
Left movements of movements of the 1960s. Yet many of today’s principled demands — for
safe spaces free of the microaggressions of overt and covert ethnoracial and gender stereotypes,
for trigger warnings to help students with past traumas prepare to engage with challenging
material — are entirely rational responses to a climate of high-stakes competition for credentials
that are now defined by perpetual uncertainty. Trapped between the contradictions of grade
inflation (which devalorizes the pursuit of elite distinction) versus rigid bell-curve meritocracy
(which devalorizes a majority of students in order to maintain the status inequalities of
‘excellence’), students endure ever more frequent episodes of sifting and sorting that constitute
an interminable assault on self-esteem. Paradoxically, self-esteem is often most at risk in
precisely those parts of the humanities and social sciences that are on the progressive frontiers of
new questions and alternative or radical ways of thinking — where there may be no consensus
on “correct” answers; in contrast, it is the most established and conservative disciplines that give
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students a clear path through the mastery of formally-sanctioned facts and theories to build
cumulative knowledge and professional confidence. (In Social Justice and the City, David
Harvey [1973] exposed the epistemological and political dynamics of this paradox between
revolutionary and counter-revolutionary Kuhnian ‘normal science’ and knowledge production).
In the most elite rounds of competition — awards, fellowships, jobs — the confidence allocated
to “winners” in the numerator is financed by the rejection of “losers” in a denominator that is
always designed to be as large as possible. The inaugural class of NYU’s Global Network
University at NYU Abu Dhabi, for instance, accepted 188 candidates from a cosmopolitan,
transnational pool of more than 9 thousand applicants. In this world of constant competition,
when the failure to win a tightly rationed supply of elite distinction can be understood through an
infinity of embodied experiences and situated identities, the macroaggressions of cognitive
capitalism’s ruthless global sorting are all too easily manifest in the microaggressions shaping
students’ everyday lives.
At the interface between these scales — between the macro-level pressures of local, national, and
transnational structures of institutional competition and the micro-level experiences of individual
students and teachers in lecture halls and seminars, in term papers and office hours and late-night
emails and unstable online application ecosystems — is the letter of recommendation.
In this essay, my purpose is to develop a critical theory of the geography of the letter of
recommendation. The geography I have in mind is not just the kind that entails mapping the
origins and destinations of students, referees, and letters; this quantitative approach, which
exemplifies what Massey and Meegan (1985) once called “extensive” research, can certainly be
interesting and worthwhile — and indeed, the first part of my analysis exploits the limited
systematic data available in an attempt to understand the contours of collective labor devoted to
the process. But the quantitative analysis serves only to document the profound social and
institutional unevenness in recommendations. To explore the meanings and implications of these
inequalities, I turn to the qualitative mode of inquiry that Massey and Meegan (1985) called
“intensive” research. Massey and Meegan’s dichotomy is usually interpreted as a defense of the
scientific legitimacy of the in-depth empirical case study, but it also provides a compelling
justification for qualitative synthesis and critical theoretical reflection. Hence the geography I
wish to explore here is a philosophical space of Kantian idealism amidst a dramatically shifting
infrastructure of capitalist production in which Marxian theories of surplus value must be
adapted to the commodification of human ideas, creativity, and hope. Under conditions of
intensified competition, accelerated cybernetic innovation, and increasing transnational mobility,
the academic reference letter assumes new roles disguised by the weight of established traditions
and taken-for-granted popular assumptions.
“It’s just a recommendation letter!” you may be thinking at this point. I challenge this notion
with a theory that includes three components. First, the reference letter has become an
instrument of expansive, multidimensional surveillant quantification. The Foucauldian
panopticon metaphor may very well be the exhausting cliché of critical social theory, but such
ubiquity in no way diminishes its explanatory power for this case. Second, the reference letter
has become a dangerous shadow zone of exploitation and discrimination. To be sure,
recommendations have always been (and always will be) constituted through subjectivity, bias,
and positionality; intensified competition and cybernetic governmentality, however, magnify and
5
transform these inequalities in particularly dangerous ways. Third, the reference letter traces the
frontier of neoliberal bureaucratization, with all its contradictions of standardized quantification
suppressing individual creativity, and platitudes of transparent accountability threatening
privacy, confidentiality, and indeed human dignity. Through it all, however, the strategically
crafted recommendation letter remains a refuge from the tyranny of endless standardized forms
that have colonized nearly every corner of academic life. Even with the proliferation of
cybernetic review infrastructures over the past decade — the pull-down menu epistemology of
the dynamic ecosystem of online platforms, each uniquely programmed by subcontractors and
consultants working to customize the automated functionality required by a particular funding
agency, employer, or university — the option to upload a traditional narrative letter usually
survives. And the letter itself still allows a space for those last few dimensions of academic
freedom that defy coercive administrative hierarchy: genuinely autonomous judgment, and the
chance to change institutions in potentially radical ways by supporting individual applicants.
This gives us reason for hope.
A few points of clarification are necessary to understand my positionality. In a short but
powerful commentary on the academic job crisis published in the Professional Geographer, Fritz
Nelson and Bria Holcomb analyzed how the “supply of qualified potential academic job seekers
exceeded the number of available jobs by a substantial margin,” forcing the “ambitious aspirant”
to submit more and more applications; this leads to “epistolary overload for applicant, referees,
and employers” (p. 78). Nelson and Holcomb advocated a few simple procedural changes —
crucially, the shift from a ‘saturation approach’ to a ‘surgical approach’ for when letters of
recommendation would be required — designed to achieve “minimization of paperwork and
humane treatment of all applicants.” Their title, “Referential Treatment,” struck me as both
playful and profound when I first read it as a struggling, deeply insecure grad student. Little did
I realize that I’d eventually get the chance to meet Nelson and Holcomb as faculty colleagues.
But that was decades ago: “Referential Treatment” was published in 1989, and I left that faculty
position in 2002. Since then, exponential escalation of academic competition in the cybernetic
“social operating system” (Rainie and Wellman, 2012) of the network society has made
“epistolary overload” a pervasive feature of academic life at all levels of the hierarchy. Not long
ago, when administrators at one U.S. public university set out to determine exactly how many
official communications were sent out just to their undergraduates during their first year, they
counted no fewer than 403 emails — “and those were just the ones that high-level administrators
knew about” (Supiano, 2016), and those are only the surface waves on the tsunami of emails,
text messages, and status updates of the full-time social media jobs now required of all “aspirant”
students and young professionals. A quarter-century after first reading Nelson and Holcomb’s
eloquent analysis, I am a tenured full Professor whose privilege must be checked and re-checked
— in terms of gender, sexuality, Whiteness, native enrollment in the linguistic colonialism of
English, access to the institutional legitimacy of a wealthy educational institution of the Global
North, the curriculum vitae inflation that builds through middle age, and every other axis of
intersectionality that I am learning to scrutinize. Yet here I wish to focus on another axis of
privilege: my career would have been impossible without all the letters of recommendation
written by advisors, supervisors, mentors, and referees over many years. I now spend a great
deal of time and attention — and so do you — writing recommendations or reading them. I take
6
the process very seriously. In a few cases the effort seems to have opened a few doors.3 But we
now find ourselves in a transnational system of accelerating, competitive epistolary overload that
is fast morphing McLuhan’s (1962) analysis of the Gutenberg-scale electrification of print
culture into a smartphone cybernetic evolutionary literacy of ubiquitous adaptive vocabulary —
where text-message shorthands like ‘tl;dr’4 are recognized amongst hundreds of millions who are
drowning in forwarded hyperlinks. In this environment — where the Harvard-MIT joint venture
online learning enterprise EdX is using artificial intelligence algorithms to automate the grading
of student essays (Markoff, 2013) — I remain terrorized by the realization that more and more of
what is written in recommendation letters is destined to be ignored, at least by caring, thoughtful,
reflective humans. When the numerator is constrained by the limits of budgets or the status
hierarchies of cultural capital, even the most detailed and compelling letters of reference for the
most exceptional applicants will be buried with the rejections issued to an infinitely expanding
denominator. Cognitive capitalism is now constituted through a harsh competitive logic of xx
1lim
that connects the situated knowledges of embodied microaggressions to the planetary
macroaggressions born of the encounter between Western and non-Western authoritarian
neoliberalisms.
Mapping the Uneven Landscapes of Recommendations
“Perhaps the college application process should be preceded by a trigger warning.
For students, it’s the season of stress. Admissions deans aren’t so fond of it,
either.”
(Pappano, 2015)
If indeed we must live in a world of intensified cognitive-capitalist competition, what can we
learn about its uneven configuration? While there are few systematic data sources beyond those
extreme cases that merit press coverage or litigation, one source of information provides a
glimpse of one of the more important and stressful educational gateways: the college / university
admissions process. In the United States, the most comprehensive source of data on the
characteristics of higher education institutions is the Integrated Postsecondary Education Data
System (IPEDS), available through the National Center for Education Statistics (NCES). The
broad coverage of IPEDS results from legal mandates requiring institutional information as a
condition of maintaining eligibility to receive students’ federal financial aid; these are not sample
data, but rather comprise a full enumeration determined by institutional and legal criteria. Many
data elements are required of thousands of universities, colleges, and training institutes, but here
we are especially interested in the questions asking institutions 1) whether they are ‘open
admission,’ accepting all paying customers, and 2) if not, what criteria are used to decide who is
offered admission. Such criteria include secondary school grades, class rank, and scores on the
3 One reference letter for an undergraduate whom I described as a thinker of “unbounded potential” who pursued
challenging intellectual and policy questions with the passionate energy “of an investigative reporter” and “the
seasoned caution of a senior scholar,” presumably played a small role in the admission decision for a graduate
program in public health and international relations at Columbia University. A decade later the student was part of
the Doctors Without Borders team of ‘Ebola Fighters’ named as Time magazine’s “Person of the Year.” See Cantor
(2014). 4 “Too long; didn’t read.” For an important response, see ‘nl;pr,’ the Preface to Naomi Baron’s (2015) magisterial
Words Onscreen. “Not long; Please read.”
7
various dominant tests of ‘achievement’ or ‘aptitude’; and then there is one more question on
admissions criteria — whether letters of reference are a) required, b) recommended, or c) neither
required nor recommended. This is just one simple categorical question that obscures the
extraordinary qualitative diversity in the kinds of insights that admissions officers at different
types of institutions seek when they actually read letters of recommendation. Nevertheless, the
inclusion of this question in the IPEDS allows us to analyze and correlate the role of
recommendations with other dimensions of a vibrant, uneven landscape of educational
competition across an entire nation-state.
I built an institution-level database composed of variables drawn from the institutional header,
instructional cost, and enrollment characteristics of all institutions reporting for the most recently
available complete edition (2014) of the IPEDS. This yields data on 7,687 separate institutions
(Table 1). Eliminating the open-enrollment shops that accept all paying (or federally subsidized)
customers narrows the database to 2,236 institutions.
8
The social and institutional landscape of recommendations is profoundly uneven. Of the 9.67
million who applied at this subset of institutions disclosing their admissions criteria, somewhat
more than a third were required to obtain letters of recommendation — translating to
approximately 3.85 million applications. Recommendations were ‘recommended’ by institutions
for another 19.3 percent of applicants. For a plurality, however — 3.95 million applicants, 40.8
percent — recommendations are neither required nor recommended: the focus is on high school
grades, class rank, test scores, or other criteria.
Table 1. Letters of Recommendation and Admissions Decisions by Basic Carnegie 2010 Classification, 2013-2014 Academic Year
Institutions reporting specified admissions criteria
Number Percentage requiring
All reporting of reporting Number of Admission recommendations
institutions institutions applicants rate for admission*
Not applicable, not in Carnegie universe (not accredited or nondegree-granting) 3,302 312 86,799 71.5 9.5
Associate's: Public Rural-serving Small 119 6 4,953 48.2 4.6
Associate's--Public Rural-serving Medium 290 4 5,082 97.2 0.0
Associate's--Public Rural-serving Large 128
Associate's--Public Suburban-serving Single Campus 107 1 383 95.8 0.0
Associate's--Public Suburban-serving Multicampus 100 1 7,851 51.3 0.0
Associate's--Public Urban-serving Single Campus 31
Associate's--Public Urban-serving Multicampus 126 1 5,632 100.0 0.0
Associate's--Public Special Use 6 2 999 38.4 6.4
Associate's--Private Not-for-profit 87 36 15,361 50.6 28.9
Associate's--Private For-profit 595 102 23,383 82.2 6.9
Associate's--Public 2-year colleges under 4-year universities 47 8 11,299 79.2 16.4
Associate's--Public 4-year Primarily Associate's 39 6 6,967 61.0 0.0
Associate's--Private Not-for-profit 4-year Primarily Associate's 17 4 3,456 70.0 80.0
Associate's--Private For-profit 4-year Primarily Associate's 89 35 11,769 78.8 0.0
Research Universities (very high research activity) 108 106 2,920,778 42.8 43.0
Research Universities (high research activity) 99 93 1,304,054 62.7 36.8
Doctoral/Research Universities 90 72 571,537 64.5 42.7
Master's Colleges and Universities (larger programs) 405 352 2,349,795 61.9 29.5
Master's Colleges and Universities (medium programs) 180 149 475,090 65.6 32.3
Master's Colleges and Universities (smaller programs) 120 86 277,137 66.7 30.7
Baccalaureate Colleges--Arts & Sciences 263 244 817,414 50.2 80.4
Baccalaureate Colleges--Diverse Fields 379 296 543,127 61.8 32.6
Baccalaureate/Associate's Colleges 136 44 88,542 69.3 0.6
Theological seminaries, Bible colleges, and other faith-related institutions 278 109 11,671 68.2 87.3
Medical schools and medical centers 50 2 47 68.1 57.5
Other health professions schools 158 33 15,205 71.7 61.4
Schools of engineering 7 5 7,411 58.9 74.0
Other technology-related schools 56 52 12,193 82.0 7.7
Schools of business and management 58 9 14,003 47.8 54.2
Schools of art, music, and design 128 62 75,486 52.4 64.4
Schools of law 36
Other special-focus institutions 20 2 2,086 67.7 100.0
Tribal Colleges 33 2 434 65.4 17.5
Total 7,687 2,236 9,669,944 55.9 39.8
*Weighted by total number of applicants at each institution.
Data Source: National Center for Educational Statistics (2016). Integrated Postsecondary Education System (IPEDS), 2014 Reporting Year.
9
Modeling Recommendations
Every year, in the U.S. alone, some four million students are required to obtain recommendations
when seeking admission to postsecondary institutions. This translates to somewhere between 7.7
and 11.5 million recommendations; some institutions (like Harvard and Stanford) require two,
while others (NYU) mandate three. Even after factoring in the replication efficiencies available
to those referees and institutions that use the Common Application,5 these millions of
recommendations constitute an enormous investment of individual and collective labor — for
high-school teachers, for students, for friends and family who counsel students through a
significant life decision, and of course for the admissions staff who are all embedded in what
Lainier (2015) has diagnosed as the “drift from a mission-driven to an admissions-driven system
of higher education.”
Is it possible to model the structural and institutional inequalities of this massive enterprise in
social classification and social sorting?
5 The Common Application is a partly standardized online application system used by nearly 700 institutions, most
but not all of them in the U.S. In 2014, the Common Application processed 3.7 million applications from 860
thousand students. After a series of website crashes and glitches in 2013, a “Coalition for Access, Affordability, and
Success” led by admissions deans at highly selective universities organized to creat an alternative system; the
endeavor quickly evolved from “a hedge against tech failures” into a much more fundamental “tool for reshaping the
admissions process” by collecting much more information about students’ educational experiences at ever earlier
points in their lives. See Pappano (2015).
Table 2. Letters of Recommendation and Admissions Decisions by Carnegie Classification of Undergraduate Profile.
Institutions reporting specified admissions criteria
Number Percentage requiring
All reporting of reporting Number of Admission recommendations
institutions institutions applicants rate for admission*
Not applicable, not in Carnegie universe (not accredited or nondegree-granting) 3,303 312 86,799 71.5 9.5
Not applicable, special focus institution 790 273 132,616 58.8 58.7
Not applicable 21
Not classified 1 1 2,308 54.8 100.0
Higher part-time two-year 431 17 1,926 65.3 8.9
Mixed part/full-time two-year 497 17 22,929 74.7 0.8
Medium full-time two-year 335 61 21,962 79.9 12.9
Higher full-time two-year 438 79 31,234 65.2 15.1
Higher part-time four-year 209 62 105,391 64.1 15.3
Medium full-time four-year, inclusive 203 114 411,582 63.4 12.8
Medium full-time four-year, selective, lower transfer-in 37 34 129,455 60.2 44.3
Medium full-time four-year, selective, higher transfer-in 112 106 767,524 57.3 5.5
Full-time four-year, inclusive 408 270 732,806 63.8 24.5
Full-time four-year, selective, lower transfer-in 282 276 1,134,600 68.3 42.3
Full-time four-year, selective, higher transfer-in 283 280 1,775,142 67.4 22.1
Full-time four-year, more selective, lower transfer-in 271 268 2,882,842 42.5 73.3
Full-time four-year, more selective, higher transfer-in 66 66 1,430,828 48.8 29.4
Total 7,687 2,236 9,669,944 55.9 39.8
*Weighted by total number of applicants at each institution.
Data Source: National Center for Educational Statistics (2016). Integrated Postsecondary Education System (IPEDS), 2014 Reporting Year.
10
Let p be the probability that a given educational institution requires recommendations from
students applying for admission. Given the “millions of ... words written about affirmative
action” before the U.S. Supreme Court’s 1978 Bakke decision and up through the 2012 Fisher
decision, and the billions of words subsequently devoted to the question of how state and
corporate authority may use social categorizations to allocate benefits among citizens
(Dershowitz and Hanft, 1979, p. 380), the first question is whether we can detect systematic
relations between recommendations and measures of gender G′ and ethnoracial R′ difference:
[1]
Gi′ is here measured as females as the percentage of the total number of students enrolled in the
Fall for each reporting institution, and Ri′measures percentages of enrolled students who identify
as Asian, Black or African American, Hispanic/Latino, American Indian or Alaska Native,
Native Hawaiian or Other Pacific Islander, two or more races, and then unreported/unknown. In
this model, the crudely oversimplified male/female Gi′ dichotomy and discrete taxonomies of
racial separation in Ri′ are the product of historical, legislative, and judicial compromises as the
Civil Rights movement sought to reverse state policies of enforcing discrimination towards
public commitments to eliminate discrimination (see Anderson and Fienberg, 2000). Put simply,
the IPEDS data reflect the limitations of historical categorizations that (even with the
information on multiracial identities and racial non-disclosure) can never capture the full
complexity of difference and intersectionality. Yet it is nevertheless important to test for these
still-pervasive gender and ethnoracial inequalities, and to evaluate whether they persist after
accounting for the competitive, regulatory, and strategic positions of educational institutions:
[2]
In this model, we add measures of institutions’ internationalization, Ii′, measured here as the
percentage of students who are not U.S. citizens and who are in the U.S. on a visa or temporary
basis.6 Ai′ measures selectivity in terms of the admissions rate, the percentage of first-time,
degree/certificate-seeking undergraduates who applied and who were subsequently admitted and
enrolled (including both full- and part-time enrollment). Institutional competitive and regulatory
circumstances, Ci′, are measured by Carnegie classifications. Developed in 1970 by a
Commission established by the Carnegie Foundation for the Advancement of Teaching, the first
‘Carnegie Classification’ quickly became “the dominant — arguably the default” (McCormick
and Zhao, 2005, p. 52) way that researchers and administrators analyzed educational institutions;
ironically, however, a taxonomy devised to measure institutional diversity soon evolved into a
force for homogenization. Administrators — and, in the case of public institutions, legislators
and governors — have “sought to ‘move up’ the classification system for inclusion among
‘research-type’ universities” (MCormick and Zhao, 2005, p. 52). In late 2005, Carnegie released
6 In the IPEDS, non-resident aliens are not included in any of the five specified racial/ethnic categories. Resident
aliens and other eligible non-citizens who have been admitted as legal immigrants for the purpose of obtaining
permanent resident alien status are included in the racial/ethnic tabulations.
iii
tionrecommenda
tionrecommenda ''p
p
RG RG0
1ln
iiiiii
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CAIRG CAIRG0
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11
a major revision to the system, featuring a set of “independent, parallel classification
frameworks” that can be likened to separate lenses through which to view similarities and
differences in missions and student populations (McCormick and Zhao, 2005, p. 56). Here, my
purpose is to evaluate how the competitive and regulatory landscapes of various institutions
shape the role of recommendations in the admissions gateway.
Finally, we add measures to capture variations in the sizes of colleges and universities (Si′, based
on the total number of students enrolled for credit in Fall, 2012) and the financial resources
required for genuine access (Fi′, the average tuition and required fees paid by full-time
undergraduates).
[3]
This iterative modeling approach is used for several reasons. It allows us to test for gender and
racial disparities in recommendations, and to see whether and how these inequalities change
when we consider the status hierarchies and functional contrasts between different kinds of
schools. It helps us to appreciate distinct yet related aspects of the vast, variegated landscape of
educational aspirations, constraints, and challenges. Logistic regression, moreover, is
comparatively robus to methodological biases such as multicollinearity — facilitating
comparisons of parameters across Models 1, 2, and 3.7
7 Menard (2002, p. 76) advises that parameter estimates should be regarded with caution when multivariate tolerance
estimates fall below 0.20. In the fully specified formulation (Model 3), only two variables slip below this threshold
— the indicators for the smallest institutions (fewer than 1,000 students) and for the next largest category (between
1,000 and 4,999 students).
iiiiiiii
tionrecommenda
tionrecommenda '''''''p
p
FSCAIRG FSCAIRG0
1ln
12
Table 3. Logistic Regression Model of Recommendation Requirements
and Institutional Characteristics.
Parameter Model 1 Model 2 Model 3
Intercept 1.130 1.145 0.027 ***
Women as share of total enrollment 1.052 1.275 *** 1.084
Student population: AsianA
1.188 *** 1.050 1.140
Student population: Black or African American 0.626 *** 0.691 *** 0.793 ***
Student population: Hispanic 0.590 *** 0.664 *** 0.852 *
Student population: American Indian or Alaska Native 0.842 0.903 0.987
Student population: Native Hawaiian or Other Pacific Islander 0.927 0.987 1.044
Student population: Two or more races 0.881 * 0.872 * 0.807 **
Student population: Race/Ethnicity unknown 0.989 1.019 0.988
Student population: Non-resident Alien 1.534 *** 1.346 ***
Admissions rate 0.746 *** 0.838 **
Carnegie Doctoral/Research Universities, ExtensiveB
0.566 * 7.907 ***
Carnegie Doctoral/Research Universities, Intensive 0.619 2.416 *
Carnegie Masters Colleges and Universities I 0.754 * 1.649 *
Carnegie Masters Colleges and Universities II 1.020 1.526
Carnegie Baccalaureate Colleges, Liberal Arts 3.907 *** 3.419 ***
Carnegie Baccalaureate Colleges, General 1.041 1.494
Carnegie Baccalaureate/Associates Colleges 0.450 0.675
Carnegie Associates Colleges 0.391 *** 0.617
Carnegie Theological Seminaries and other specialized faith-related institutions 14.646 *** 23.271 ***
Institution size: under 1,000C
11.025 ***
Institution size: 1,000-4,999 7.279 ***
Institution size: 5,000-9,999 7.325 ***
Institution size: 10,000-19,999 3.400 **
In-state average tuition and required fees for full-time undergraduates 2.253 ***
Out-of-state costs as percentage of in-state tuition and fees 0.864
Number of observations 2,216 2,216 2,034
Max-rescaled Nagelkerke Pseudo-R-squared 0.104 0.274 0.421
Percent concordant 68.4 76.0 83.2
*Significant at P<0.05; **P<0.01; ***P<0.001.
AReference category for race/ethnicity is non-Hispanic White.
BReference category for institution type is all other Carnegie classifications,
including engineering, technology, business, art, teachers colleges, tribal colleges,
and medical schools and other health professions schools.
CReference category for institution size is 20,000 and over.
Note: odds ratios for continuous variables are standardized odds ratios, reporting the change in odds with a one-standard
deviation increase in the respective predictor variable.
Data Source: National Center for Educational Statistics (2016). Integrated Postsecondary Education System (IPEDS),
2014 Reporting Year.
13
This modeling approach reveals a starkly uneven socio-institutional landscape (Table 3). First
consider Model 1, which focuses on the ethnoracial inequalities that have always been so central
to American educational stratification. The most striking disparity appears between Asians
versus Blacks and Hispanics. Institutions with higher shares of Asian students are more likely to
require letters of recommendation in the admissions decision, while schools more focused on
African Americans and Latinas/Latinos rely more heavily on test scores and other criteria.
Increasing a school’s Asian population share by one standard deviation (from the overall average
of 3.89 percent to 9.42 percent) increases the odds of a recommendation requirement by a factor
of 1.19. Conversely, a one-standard-deviation increase in a college or university’s Black
population share (from an average of 14.2 percent to 32.9 percent) reduces the odds of a required
recommendation by more than a third (to an odds ratio of 0.63). For Latinas and Latinos, the
corresponding change in an institutional student population share (from 10.8 percent to 28.2
percent) suppresses the recommendation likelihood even further (to an odds ratio of 0.59).
Compared with the Asian / Black and Hispanic divide, other ethnoracial divisions are much
smaller (e.g., the standardized multiracial odds ratio of 0.88) or statistically insignificant; there is
also no evidence of significant gender inequality in recommendation requirements.
Nevertheless, we cannot rely too heavily on the results from Model 1, given the terribly weak
model fit (note the pseudo-R-squared measure barely over 10 percent).
Model 2, which adds an array of institutional characteristics, achieves a more robust and
appropriate level of fit (pseudo-R-squared of 27.4 percent). Several aspects of the results stand
out. The reduced recommendation odds for Black- and Hispanic-focused institutions persist,
with the odds ratios moderating only a tiny bit compared with the first model (from 0.63 to 0.69
for African Americans, and 0.59 to 0.66 for Latinas and Latinos). For Asians, however, it
becomes clear that the increased recommendation requirements are the product of a close
correlation with institutions’ pursuit of the expanding markets of transnational education. The
coefficient for an institution’s Asian population becomes statistically insignificant when we add
the school’s share of ‘non-resident aliens’ as a control variable. Increasing a school’s population
of international students by one standard deviation (from the average of 4.48 percent to 11.3
percent) boosts the likelihood that the institution will require recommendations by a factor of
1.53. At the same time, adding institutional characteristics seems to reveal a considerable
gendering of higher education admissions: if we compare colleges and universities with the
same kinds of selectivity and missions of teaching and research, institutions more focused on
female undergraduates are more likely to require letters of recommendation. A one-standard-
deviation increase in an institution’s share of women (from the average of 54.5 percent to 74.8
percent) increases the likelihood of an institutional recommendation requirement by more than a
quarter (odds ratio of 1.27). As for the institutional characteristics themselves (measured here
with the simpler, traditional Carnegie 2000 classifications to avoid the modeling complications
when certain categories include few observations), the results highlight enormous differences in
mission and focus. Perhaps not surprisingly, more selective institutions place greater emphasis
on letters of recommendation. A one-standard-deviation increase in a school’s admission rate
(from the average of 68.4 percent up to 89.2 percent) reduces the odds of recommendation
requirements by a quarter (odds ratio of 0.75). By institution type, recommendations serve a
crucial gateway function for undergraduate liberal arts colleges and for faith-based institutions.
Compared with other Carnegie classifications of institutions with the same selectivity and the
same kinds of undergraduate student populations, Baccalaureate Liberal Arts colleges are almost
14
four times more likely to require recommendations to gain entry. The odds ratio skyrockets to
14.6 for theological seminaries and other faith-related institutions. This provides one
quantitative symptom of the curious blend of research, pedagogy, faith, and politics in America.
There are progressive theological seminaries, but then there are the regressive Republican-Right
farm teams like Jerry Falwell’s Liberty University8 and Pat Robertson’s Regent University Law
School. Regent provided a steady stream of graduates to George W. Bush’s Justice Department
in the era of Abu Ghraib, all while proclaiming its officially non-discriminatory admissions
policy: “...we admit all students without discrimination,” explained Monica Goodling, one of the
150 Regent graduates who served in the Bush Administration; “We are a Christian institution; it
is assumed that everyone in the classes are Christians” (quoted in Lithwick, 2007).
Model 3 incorporates several other crucial considerations in the social selectivity and social
sorting of educational cultures: cost, and the overall size of the institution as measured by the
total number of students enrolled for credit as of Fall, 2012. This extended model achieves
robust fit (a pseudo-R-squared of 42 percent), and it significantly alters the substantive
interpretation of various aspects of admissions. To begin with, the effects of tuition and fees
highlight the foundational role of class reproduction in elite educational arenas. A one-standard-
deviation increase in required in-state tuition and fees (from the average of $20,307 to $32,324)
more than doubles the likelihood that letters of recommendation will be required for admission
(odds ratio of 2.25). Notably, while the additional controls in Model 3 have only a modest effect
on institutions’ transnational profiles as measured by the percentage of non-resident aliens
(reducing the odds ratio from 1.53 to 1.35), there is no statistically significant role for the tuition
differential between local and non-local students. Recommendation requirements are correlated
with more expensive institutions and with international student enrollments, but there is no
evidence that recommendations are associated with the explicit targeted pursuit of more ‘distant’
students as sources of revenue.9 Institutional size, however, has a dramatic effect in highlighting
the division of labor between research and teaching schools. While research-focused universities
do not stand out on their recommendation requirements in Model 2 (posting odds ratios of 0.57
and 0.75 compared to other types of institutions), this is purely an artefact of the enormous scale
of most Doctoral/Research institutions. Two-thirds of all Doctoral/Research ‘Extensive’
institutions have more than 20,000 enrolled students, and well over 90 percent have at least
10,000; and only 18 percent of institutions with more than 20,000 students require
8 Despite the traditional conservative evangelical hostility to government ‘welfare,’ Liberty and similar institutions
happly encourage their students to complete the FAFSA (Free Application for Federal Student Aid), most recently
advising 2017-2018 students to complete the new ‘Early FAFSA’ made available on October 1, 2016 — three
months earlier than the traditional FAFSA. Only two weeks later, a group of students — ‘Liberty United Against
Trump’ — issued a statement signed by 1,300 students, faculty, and alumni challenging Liberty President Jerry
Falwell, Jr.’s endorsement of Trump’s presidential bid. The students’ statement noted that “while everyone is a
sinner and everyone can be forgiven, a man who constantly and proudly speaks evil does not deserve our support for
the nation’s highest office.” Jerry Falwell, Jr. — son of the founder of an institution that is a politically powerful
“epicenter of evangelical education” in the U.S. as well as one of the nation’s largest based on expanding online
offerings — issued a series of angry and condescending replies. “The group of students ... represents a very small
percentage of the Liberty student body of 15,000 resident students and 90,000 online students ... This student
statement seems to ignore the teachings of Jesus not to judge others but they are young and still learning.” See
Shapiro et al., 2016. 9 ‘Out of state’ costs as measured in the IPEDS refer to costs imposed on all students not meeting specified
residency requirements; these requirements are sometimes determined by institutions, and sometimes by State
legislation.
15
recommendations, compared with more than 40 percent of institutions enrolling fewer than
1,000, and 45 percent for institutions between 1,000 and 5,000. If we compare institutions with
the same sizes, and while holding constant all other elements in Model 3, then Doctoral/Research
Extensive universities do place a greater emphasis on recommendations. Given the multiple
interdependencies between economies of scale and state power in contemporary research,
however, in practice it is hard to find the small research institutions that we would expect to
place as much emphasis on qualitative recommendations as small liberal arts schools. In the
Doctoral/Research Extensive category, there is only one with enrollments between 1,000 and
5,000 — Caltech, with its admissionss rate of 8.83 percent and a non-resident alien student
population of 27.3 percent — does require recommendations for admission. There are no
Carnegie Doctoral/Research Extensive institutions with enrollment of fewer than 1,000 students
— the size category in which, all else being equal, an institution is over ten times more likely to
require recommendations compared to the largest schools. There are more institutions in the
middle-to-small size ranges in the ‘Intensive’ research universities and higher-tier Master’s
institutions, and these schools (holding all other factors constant) are more likely to require
recommendations: note the odds ratio of 2.42 for the former and 1.65 for the latter. It is also
noteworthy that gender and ethnoracial divisions recede or disappear once we control for cost
and institutional scale. The recommendation odds ratios edge closer to parity from Model 2 to
Model 3 for African Americans (0.69 to 0.79) and Hispanics (0.66 to 0.85) although the
disparities do remain statistically significant. The ratio for Asians remains statistically
insignificant across both Model 2 and Model 3. The odds ratio for female enrollment reverts to
statistical insignificance — implying that the apparent gender divide in research and teaching
school hierarchies in Model 2 is closely intertwined with institution size and cost. If we compare
expensive schools focused on male versus female enrollments, for example, very similar
proportions of the institutions require recommendations for admission (76.9 percent for the
former, 79.6 percent for the latter).10
Finally, adding the cost and size controls has very little
effect on Baccalaureate Liberal Arts institutions admissions — the odds ratio goes from 3.91 in
Model 2 to 3.42 in Model 3 — but reinforces the stark contrasts between secular education and
religious institutions. The odds ratio for theological seminaries and other faith institutions jumps
from 14.6 to 23.3.
Selective Inequalities
There’s another way to view the unequal spaces of recommendations. Quick: imagine you’re on
an admissions committee for a graduate program, and as you sift through applications, over and
over again you see variations on a familiar form of praise: “...has achieved distinction at our
competitive, highly selective institution...” What do we really mean by “highly selective”? What
are the biases and inequalities hidden behind this presumptively neutral descriptor? Let p be the
probability that an institution meets two of the most important criteria of distinction: cost and
admissions rate. I identified institutions in the IPEDS database as ‘elite’ if they fell in the top 5
percent in terms of required tuition and percentage of applicants rejected. Now: is the likelihood
of elite, selective distinction p related to systematic inequalities in gender (Gi′) race and ethnicity
10
In this comparison, ‘expensive’ is defined as annual costs more than one standard deviation above the average
(over $32,324), ‘male focused’ institutions are defined as enrollments over 55 percent male, and ‘female focused’
institutions are defined as more than 55 percent female enrollment.
16
(Ri′) while holding constant the effects of social sorting through a previous round of letters of
recommendations (LORi′) just like the ones you’re reading for that admissions committee?
[4]
Viewing the academic landscape through this lens highlights a sharp partitioning (Table 4).
While there is no clear gender bias — women as a percentage of a school’s student population is
not a statistically or substantively significant predictor of elite status, as the standardized odds
ratio is very close to parity (0.95) — ethnic and racial disparities are pronounced. A deep divide
separates historic and contemporary axes of America’s changing racial economy. On the one
hand, elite institutions are at the advancing edge of a ‘Pacific Century’ (Ley, 2010) of Asian
identities, transnationalism, and multiracial hybridity. A one standard deviation increase in the
proportion of a school’s students identifying as Asian boosts the likelihood of a school’s elite
status by a factor of 1.46. The corresponding ratio for multiracial students is 1.71, and for non-
resident aliens it is 1.34. On the other hand, educational distinction continues to reflect the
historically entrenched inequalities of the U.S. role in Latin America and the dispossession of
indigenous peoples and African Americans. A one standard deviation increase in a school’s
share of African Americans cuts the odds of elite distinction down to a ratio of 0.32. There
seems to be some contingency in this quantitative effect — the coefficient is only moderately
significant (P=0.0542) — but the fact remains that not a single elite institution has a Black
population share that even meets the overall average (14.55 percent) for all institutions. Among
elite schools, the highest percentage (12.39 percent) is attained by Amherst College, a small
residential baccalaureate liberal arts college in Western Massachusetts. For Latinos and Latinas,
the odds ratio is 0.80 but with a statistically unstable coefficient (P=0.6159). On average,
Hispanic students comprise 8.51 percent of the student population at elite institutions (with a
standard deviation of 2.70 percent) compared with 11.58 percent at all non-elite schools
(sd=17.59). The highest share (15.15 percent) appears at Pitzer College, part of the Claremont
Colleges in the foothils east of Los Angeles. For American Indians and Alaska Natives,
increasing a school’s representation by one standard deviation (3.1 percent) slashes the elite odds
down to a factor of 0.15. The model coefficient associated with this effect is unstable (0.2104),
but the unconditional figures are nevertheless notable: Native Americans comprise only one
quarter of one percentage point of students at elite universities. The highest share — 1.62
percent — is at Dartmouth, rare among the Ivy League with a full Native American Studies
Program, with faculty specializing on research and teaching on tribal communities (Karr, 2016).
iiii
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17
Quantified Spaces of Recommendation
All things considered, what role does geography play in the institutional landscape of
recommendations? Among the many alternative ways of conceptualizing and measuring
geography, here we consider two: straightforward institutional location, and multivariate
factorial ecology.
First we measure institutional location by returning to our most detailed logistic model of
recommendations — Table 3, Model 3 — and adding a set of dichotomous indicators for the
regional information coded in the IPEDS. Results are shown in Table 5. This approach
highlights the historically accumulated socio-institutional networks of New England. Compared
with the reference category (Rocky Mountain states and the service academies of America’s
worldwide military footprint) institutions in New England are more than two and one half times
more likely to require recommendations to gain admission. This effect persists after considering
all other factors — selectivity, cost, size, and competive and regulatory position within the
Table 4. Logistic Regression Model of Elite Access
Parameter
Intercept -8.816
Women as share of total enrollment 0.947
Student population: AsianA
1.457 ***
Student population: Black or African American 0.317 *
Student population: Hispanic 0.805
Student population: American Indian or Alaska Native 0.152
Student population: Two or more races 1.704 ***
Student population: Race/Ethnicity unknown 0.913 ***
Student population: Non-resident Alien 1.340
Letters of Recommendation Required 211.4 ***
Number of observations 2,216
Max-rescaled Nagelkerke Pseudo-R-squared 0.357
Percent concordant 94.0
*Significant at P<0.10; **P<0.01; ***P<0.001.
AReference category for race/ethnicity is non-Hispanic White; Native Hawaiian
and OPI excluded due to low and zero values.
Note: odds ratios for continuous variables are standardized odds ratios,
reporting the change in odds with a one-standard deviation increase in the
respective predictor variable.
Data Source: National Center for Educational Statistics (2016). Integrated
Postsecondary Education System (IPEDS), 2014 Reporting Year.
18
societal division of educational labor. Notably, this effect does not extend south of Connecticut:
the mid-Atlantic institutions exhibit no statistically significant contrasts in their use of
recommendations. Conversely, recommendation requirements are significantly reduced — after
controlling for all other institutional characteristics — across most of the remainder of U.S.
territory. Holding all else constant, the odds of an institution requiring a recommendation fall to
0.26 in the Great Lakes and the Southwest, to 0.24 in the Southeast, and to 0.21 in the Plains
states. Three quarters of a century after Harvard President James Conant famously pushed aside
a flood of glowing recommendation letters in a faculty promotion case and declared that
Geography was ‘not a university subject’ (see Smith, 1987), Geography remains a crucial aspect
of the social and institutional screening of America’s educational landscape.
For a second type of spatial quantification, we look beyond the deductive causal logic of
regression techniques in favor of a more inductive view of multiple, interdependent correlations.
This involves a set of multivariate techniques — principal components analysis and factor
analysis — refined by educational psychologists in the 1930s and subsequently applied across
the humanities and social sciences with the parallel consolidation of positivist epistemology,
Table 5. Regional Contrasts in Recommendation Requirements
New England: CT ME MA NH RI VT 2.578 *
Mid East: DE DC MD NJ NY PA 1.019
Great Lakes: IL IN MI OH WI 0.262 ***
Plains: IA KS MN MO NE ND SD 0.209 ***
Southeast: AL AR FL GA KY LA MS NC SC TN VA WV 0.244 ***
Southwest: AZ NM OK TX 0.264 ***
Far West: AK CA HI NV OR WA 0.874
Outlying areas: AS FM GU MH MP PR PW VI 1.228
Reference category includes institutions in Rocky Mountains states of
CO, ID, MT, UT, and WY; U.S. military Service Schools; and institutions
for which regional information is not available.
Includes controls for all effects shown in Model 3, Table 3
Number of observations 2,034
Max-rescaled Nagelkerke Pseudo-R-squared 0.494
Percent concordant 86.4
*Significant at P<0.05; **P<0.01; ***P<0.001.
Data Source: National Center for Educational Statistics (2016). Integrated
Postsecondary Education System (IPEDS), 2014 Reporting Year.
Odds ratios from regional location of institution
19
quantitative analysis, and state-funded deployment of mainframe computers in Cold War
American social research in the 1950s (Steinmetz, 2005). In geographical research, these
methods conditioned the emergence of a distinctive ‘Chicago School’ of urban geography that
evolved alongside the influential Chicago School of Sociology (Berry, 2005). The fusion of a
grandiose disciplinary attempt to define the field as ‘human ecology’ (Barrows, 1923)11
with the
dazzling possibilities of computerized applications of factor analysis created an influential
stream of research that came to be known as urban factorial ecology (Berry, 1971; Berry and
Kasarda, 1977). In its most ambitious formulation — as “a methodology, a philosophy of social
science, and a substantive orientation” (Berry, 1971, p. 209) — the approach relied on the
conceptualization of factor analysis as a technique to distill the confusion of multiple surface
indicators (say, of the characteristics of countries or neighborhoods) into a set of latent,
underlying dimensions of social or spatial realities (say, national character or history,
urbanization). Given a set of many separate but related indicator variables, factor analysis
reconfigures the shared covariance into a smaller set of composite variables, or ‘factors’ —
promising a theoretically powerful breakthrough that could finally address the limits of
empiricism in gaining access to the fundamental epistemological claims of metaphysical realism
and positivist reality. Brian J.L. Berry, the most widely-cited Geographer in the world for the
first quarter-century of the existence of the Social Sciences Citation Index, suggested a
“generalized fatorial apparatus” integrated with a continuing ‘philosophical dialectic’ and
‘phenomenological base’ to “become a new watershed in which new rounds of investigation will
assume new forms and will peel back new layers in the search for truth” (Berry, 1971, p. 219).
Such wild ambitions were short-lived, in large part because of a wider politicization connecting
positivist quantification with conservative, market-oriented politics — especially the kind of
authoritarian politics common in the West, and especially in the United States, as pursued by the
White guys who held all the power (see, especially, Berry, 1972 vs. Harvey, 1973). At the same
time factor analysis (along with all sorts of other complicated multivariate procedures) became
successively easier to do with each generational advance in hardware and software — such that
cutting-edge theoretical and methodological debates gravitated elsewhere (Adams, 2005).
Paradoxically, as critical theorists in Geography and other fields turned away from quantification
in the 1980s and 1990s, the proliferation of easy templates and software solutions drove an
explosion in factorial-ecological analysis in the private geodemographic marketing industry —
formalizing an institutional rationality of ‘We Know Who You Are and We Know Where You
Live’ (Goss, 1995) that became the predecessor of today’s data mining, ‘Big Data,’ and other
aspects of contemporary “Surveillance Capitalism” (Foster and McChesney, 2014). Now widely
11
Such grandiosity was a defining feature of the attempt to claim a space for Geography in the growth of
universities in the United States. Initially, Geography was seen as only a part of Geology; the effort to define a
distinctive domain for Human Geography took off as the American academy flourished in the early twentieth
century, with the expansion of History, Economics, Political Science, Psychology, and other fields of social science.
Delivering a Presidential Address to the Association of American Geographers in 1922 — just as the Chicago
School of Sociology was skyrocketing to prominence as a new and distinctively American mode of social science —
Harlan Barrows delivered a manifesto that struggled to claim space for Geography. Among the grandiose
assertions: Geography is the “Mother of Sciences,” a “recognized study whose field embraced the entire universe”
in the “centuries before Christ.” The Mother of Science “bore many children, among them astronomy, botany,
ecology, geology, meteorology, archaeology, and anthropology.” Many of Geography’s “offspring have pursued
independent careers in the world of science for so long” that everybody forgot about Mommy. “Each child became
a successful specialist” by “taking over a part of the parental estate and working it more intensively than the parent
had done.” (Barrows, 1923, p. 1).
20
used in semi-automated classification algorithms and pattern-recognition systems, factor analysis
has evolved from philosophical breakthrough to the “witless replications of methodologies and
analyses long since generalized” that Berry (1971, p. 219) feared.
In light of this politicized methodological history, my purpose here is deliberately modest. To
complement the regression results presented thus far — attempting to predict single, discrete
dependent-variable outcomes (recommendations required, elite status) as a function of a set of
independent predictor variables — I use factor analysis to pose a different set of questions. How
are all of the available measures of institutional differences correlated with one another? Are
recommendations, along with other aspects of educational stratification, manifestations of
deeper, structural patterns of difference among thousands of educational institutions? To explore
these questions, I apply the most widely-used variant of factorial ecology (see Berry and
Kasarda, 1977; Davies and Murdie, 1991; Wyly, 1999) to the full IPEDS database variables in
the most detailed specification (Table 3, Model 3), along with the regional indicators (Table 5).
Additionally, we incorporate several enhanced institutional measures made available as part of a
landmark study of the role of educational institutions in upward social mobility (Chetty et al.,
2017). Drawing on extraordinarily detailed enrollment data linked to Internal Revenue Service
federal tax return data for the families of more than 30 million college students between 1999
and 2013, researchers at Stanford, UC-Berkeley, and the National Bureau of Economic Research
(NBER) created a series of “mobility report cards” — “statistics on students’ earnings in their
early thirteis and their parents’ incomes — for each college in America” (Chetty et al., 2017, p.
1). I incorporated six of these measures of student and family income inequality and outcomes,
linking the variables to the rest of the IPEDS database.
After excluding missing data items, the analysis incorporates 40 variables on 1,999 institutions.
Principal components analysis of the correlation matrix yeilds 18 eigenvalues exceeding the
standard 1.00 threshold; these eighteen are retained and subjected to Varimax rotation, producing
a rotated factor pattern that accounts for 71.6 percent of the total variance in the full dataset.
Table 6 presents the rotated factor pattern, highlighting loadings more than 0.40 in either
direction from zero.
Factorial Ecology Results
The results of this analysis give us a flood of eye-glazing numbers, like all of the massive tables
you see when you read through the history of the factorial ecology literature. Among the many
details of pattern and process, cause and consequence reflected in these numbers, however, three
sets of findings stand out as especially important. First, institutional policies of recommendation
are an important — although not dominant — dividing line in the diversity of opportunities and
inequalities in American higher education. While the total 18-factor solution can account for
only three-fifths of the variation in recommendation policies (see the communality estimate on
the recommendation variable, 0.61), this indicator loads fairly strongly on the decisive first factor
(Factor I, loading=0.65). This first factor is a generalized dimension of social selectivity and
exclusivity, with the strong recommendation loading accompanied by strong positive loadings
for Baccalaureate liberal arts colleges (+0.53) and tution (+0.77). The loading for the differential
between out-of-state and in-state tuition (-0.69) reinforces the results from the earlier logistic
analysis: the more expensive schools are not the ones seeking to raise revenues by charging
21
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.03
0.1
0-0
.01
0.0
4-0
.71
-0.0
7-0
.02
-0.0
2-0
.02
-0.0
10.0
0-0
.03
0.0
30.5
3
r_asn
Stu
den
t p
op
ula
tio
n:
Asi
an0.0
50.0
20.0
30
.67
0.4
10.0
40.0
20.0
4-0
.09
-0.0
7-0
.02
0.0
8-0
.07
0.1
3-0
.02
0.0
0-0
.06
0.0
10.6
8
r_b
lkS
tud
ent
po
pu
lati
on
: B
lack
or
Afr
ican
Am
eric
an-0
.09
-0.1
40.1
6-0
.15
-0.1
2-0
.03
0.0
10.0
00
.62
-0.2
0-0
.03
-0.0
1-0
.02
0.0
2-0
.14
0.1
6-0
.22
-0.0
20.6
1
r_h
sp
Stu
den
t p
op
ula
tio
n:
His
pan
ic-0
.09
0.8
8-0
.02
0.1
2-0
.02
0.0
00.0
4-0
.01
-0.0
9-0
.06
-0.0
10.0
40.1
40.0
2-0
.03
0.0
7-0
.14
-0.0
40.8
6
r_n
tvS
tud
ent
po
pu
lati
on
: A
mer
ican
In
dia
n o
r A
lask
a N
ativ
e-0
.03
-0.0
70.0
50.0
0-0
.06
0.0
6-0
.01
0.0
1-0
.03
0.0
00.0
3-0
.08
0.6
4-0
.07
0.0
0-0
.05
0.1
60.0
20.4
7
r_h
wi
Stu
den
t p
op
ula
tio
n:
Nat
ive
Haw
aiia
n o
r O
ther
Pac
ific
Isl
and
er-0
.04
0.2
50.0
70.0
7-0
.03
-0.0
2-0
.08
0.0
5-0
.04
-0.0
9-0
.03
-0.1
4-0
.23
-0.0
5-0
.06
-0.3
30.2
90.0
40.3
7
r_m
ltS
tud
ent
po
pu
lati
on
: T
wo
or
Mo
re R
aces
-0.0
2-0
.18
-0.0
90
.65
0.0
1-0
.02
-0.0
9-0
.02
-0.0
7-0
.15
-0.0
4-0
.03
0.1
6-0
.14
-0.0
3-0
.24
0.0
0-0
.14
0.6
3
r_u
nk
Stu
den
t p
op
ula
tio
n:
Rac
e/E
thn
icit
y U
nk
no
wn
0.1
1-0
.14
-0.0
30.1
1-0
.11
-0.0
10.0
6-0
.02
-0.0
6-0
.14
0.0
4-0
.01
-0.1
60.0
30
.52
0.2
50.0
50.1
80.4
8
r_n
raS
tud
ent
po
pu
lati
on
: N
on
resi
den
t al
ien
0.2
5-0
.06
0.0
10.1
10
.61
-0.0
40.0
6-0
.04
-0.1
20.0
5-0
.03
0.2
10.0
30.0
40.0
10.0
20.0
40.0
00.5
2
ad
rate
Ov
eral
l ad
mis
sio
ns
rate
-0.2
7-0
.05
0.1
3-0
.09
-0.5
30.0
0-0
.02
-0.0
1-0
.28
0.2
30.0
3-0
.01
0.0
0-0
.03
0.0
2-0
.11
0.0
40.0
90.5
4
c_
dc1
Car
neg
ie D
oct
ora
l/R
esea
rch
Un
iver
siti
es,
Ext
ensi
ve
-0.2
7-0
.06
-0.0
10.0
20
.80
0.0
2-0
.14
-0.0
2-0
.09
0.0
30.0
6-0
.12
-0.0
3-0
.05
0.0
1-0
.10
-0.0
40.0
00.7
9
c_
dc2
Car
neg
ie D
oct
ora
l/R
esea
rch
Un
iver
siti
es,
Inte
nsi
ve
0.0
10.0
20.0
20.0
3-0
.03
0.0
00.0
10.0
40.0
2-0
.01
0.0
30
.91
0.0
0-0
.05
-0.0
20.0
2-0
.01
0.0
40.8
4
c_
ms1
Car
neg
ie M
aste
rs C
oll
eges
an
d U
niv
ersi
ties
I-0
.19
-0.0
7-0
.25
0.0
1-0
.12
-0.0
30
.75
-0.0
7-0
.08
-0.1
3-0
.09
-0.0
8-0
.01
0.1
1-0
.01
-0.1
10.0
1-0
.15
0.7
7
c_
ms2
Car
neg
ie M
aste
rs C
oll
eges
an
d U
niv
ersi
ties
II
-0.0
1-0
.03
-0.1
5-0
.05
-0.0
50.0
1-0
.07
-0.0
1-0
.01
-0.0
2-0
.03
-0.0
20.0
3-0
.01
0.0
1-0
.06
-0.0
20
.91
0.8
6
c_
bcl
Car
neg
ie B
acca
lau
reat
e C
oll
eges
, L
iber
al A
rts
0.5
3-0
.03
-0.3
20.0
4-0
.07
0.0
5-0
.18
-0.0
10.1
2-0
.02
0.0
8-0
.02
0.0
4-0
.12
-0.0
9-0
.45
-0.0
4-0
.23
0.7
3
c_
bcg
Car
neg
ie B
acca
lau
reat
e C
oll
eges
, G
ener
al0.0
30.0
9-0
.21
-0.1
1-0
.03
0.0
3-0
.19
0.0
30.0
9-0
.03
0.0
3-0
.13
-0.0
5-0
.12
-0.0
30
.70
0.2
2-0
.14
0.7
1
c_
bca
Car
neg
ie B
acca
lau
reat
e/A
sso
ciat
es C
oll
eges
-0.1
60.0
2-0
.10
-0.0
1-0
.09
-0.0
8-0
.17
0.0
0-0
.04
0.0
3-0
.10
-0.0
30.0
50.1
00
.50
0.0
7-0
.26
-0.2
30.4
9
c_
ass
Car
neg
ie A
sso
ciat
es C
oll
eges
-0.1
30.0
00
.45
-0.0
2-0
.18
-0.0
4-0
.27
-0.0
40.0
4-0
.37
0.0
1-0
.11
-0.0
30.1
50.0
4-0
.21
0.0
60.0
10.5
5
c_
the
Car
neg
ie T
heo
logi
cal
Sem
inar
ies
and
oth
er s
pec
iali
zed
fai
th-r
elat
ed i
nst
itu
tio
ns
0.0
8-0
.05
0.2
0-0
.05
-0.0
8-0
.04
-0.0
30.0
1-0
.04
0.7
5-0
.11
-0.0
7-0
.06
0.0
7-0
.09
-0.0
1-0
.01
0.0
10.6
6
siz
e1
Inst
itu
tio
n s
ize
un
der
1,0
00
0.1
2-0
.02
0.8
30.0
0-0
.19
0.0
1-0
.24
0.0
30.0
40.1
9-0
.02
-0.1
40.0
8-0
.02
-0.0
50.0
0-0
.02
-0.1
00.8
7
siz
e2
Inst
itu
tio
n s
ize
1,0
00
-4,9
99
0.2
70.0
4-0
.75
-0.0
3-0
.22
0.0
0-0
.26
-0.0
10.0
2-0
.14
-0.0
1-0
.19
-0.0
40.0
40.0
50.1
30.0
30.1
50.8
6
siz
e3
Inst
itu
tio
n s
ize
5,0
00
-9,9
99
-0.0
20.0
40.0
70.0
00.0
20.0
00
.83
-0.0
20.0
3-0
.04
0.0
70.0
30.0
0-0
.05
0.0
30.0
0-0
.03
0.0
40.7
0
siz
e4
Inst
itu
tio
n s
ize
10
,00
0-1
9,9
99
-0.3
5-0
.07
-0.1
0-0
.04
0.2
3-0
.02
-0.0
5-0
.07
-0.0
9-0
.06
-0.0
60.5
3-0
.07
0.0
80.0
0-0
.19
0.0
9-0
.17
0.5
8
co
st1
In-s
tate
av
erag
e tu
itio
n a
nd
req
uir
ed f
ees
for
full
-tim
e u
nd
ergr
adu
ates
0.7
7-0
.18
-0.1
90.1
10.1
5-0
.03
-0.0
8-0
.06
-0.1
4-0
.18
0.0
30.0
0-0
.07
0.0
10.1
20.0
1-0
.02
0.0
10.7
9
co
st2
Ou
t-o
f-st
ate
cost
as
per
cen
tage
of
in-s
tate
co
sts
-0.6
9-0
.01
-0.2
20.0
90.2
40.0
60.1
90.0
40.1
30.0
40.0
30.1
70.0
4-0
.06
-0.0
1-0
.23
-0.0
4-0
.05
0.7
5
reg
_n
eR
egio
n:
New
En
glan
d0.2
10.0
10.0
3-0
.13
0.1
40.0
30.0
90.0
0-0
.05
-0.0
20.0
20.0
1-0
.01
-0.1
80
.75
-0.1
90.0
30.0
20.7
3
reg
_m
eM
id E
ast
0.1
2-0
.08
0.0
1-0
.13
0.0
20.0
10.0
1-0
.01
-0.1
70.0
60.0
6-0
.02
-0.1
20
.88
-0.1
2-0
.04
-0.1
3-0
.02
0.9
0
reg
_g
lG
reat
Lak
es0.0
0-0
.21
0.0
3-0
.21
-0.0
6-0
.03
-0.0
3-0
.05
-0.5
1-0
.13
-0.0
1-0
.02
-0.2
2-0
.52
-0.2
80.0
9-0
.40
-0.0
10.9
3
reg
_p
lP
lain
s-0
.02
-0.1
2-0
.02
-0.0
8-0
.04
0.0
0-0
.02
0.0
0-0
.15
0.0
3-0
.04
0.0
40.0
5-0
.07
-0.0
50.1
50
.82
-0.0
30.7
6
reg
_se
So
uth
east
-0.1
5-0
.11
-0.0
7-0
.12
-0.0
2-0
.01
-0.0
40.0
10
.85
0.0
1-0
.01
-0.0
2-0
.13
-0.1
8-0
.05
-0.0
4-0
.05
0.0
00.8
3
reg
_sw
So
uth
wes
t-0
.07
0.1
00.0
2-0
.01
0.0
5-0
.04
0.0
00.0
0-0
.05
-0.0
3-0
.07
0.0
60
.79
0.0
1-0
.07
0.0
4-0
.14
0.0
00.6
9
reg
_fw
Far
Wes
t0.0
20.0
90.0
50
.88
-0.0
30.0
30.0
60.0
5-0
.05
0.0
60.0
2-0
.01
-0.0
8-0
.07
0.0
00.0
4-0
.02
0.0
30.8
2
reg
_o
aO
utl
yin
g A
reas
-0.0
60
.91
-0.0
3-0
.13
-0.0
50.0
1-0
.04
-0.0
1-0
.06
-0.0
2-0
.03
-0.0
4-0
.10
-0.0
5-0
.03
-0.0
30.0
30.0
00.8
8
cx_
lia
Lo
w-I
nco
me
Acc
ess:
% o
f P
aren
ts i
n B
ott
om
Qu
inti
le-0
.03
0.0
10.0
10.0
5-0
.03
-0.1
8-0
.02
0.8
30.0
30.0
40.1
40.0
3-0
.01
0.0
0-0
.04
0.0
10.0
10.0
00.7
5
cx_
1p
ct
% o
f P
aren
ts i
n T
op
1%
0.0
10.0
2-0
.02
-0.0
20.0
0-0
.05
0.0
6-0
.76
0.0
00.0
10.3
80.0
2-0
.03
0.0
2-0
.04
-0.0
10.0
00.0
10.7
3
cx_
mo
bM
ob
ilit
y R
ate:
% o
f C
hil
dre
n w
ho
Co
me
Fro
m B
ott
om
Qu
inti
le a
nd
Rea
ch T
op
Qu
inti
le-0
.02
-0.0
50.0
0-0
.03
-0.0
1-0
.23
0.0
30
.48
-0.0
20.0
10
.70
-0.0
1-0
.04
0.0
6-0
.01
-0.0
2-0
.01
0.0
00.7
9
cx_
utm
Up
per
-Tai
l M
ob
ilit
y R
ate:
% o
f C
hil
dre
n w
ho
Co
me
Fro
m B
ott
om
Qu
inti
le a
nd
Rea
ch T
op
1%
-0.0
1-0
.01
-0.0
1-0
.01
0.0
1-0
.05
0.0
0-0
.28
-0.0
2-0
.03
0.8
40.0
1-0
.01
0.0
20.0
00.0
3-0
.03
-0.0
20.7
9
cx_
up
1C
han
ge i
n %
of
Par
ents
fro
m B
ott
om
Qu
inti
le,
19
80
-91
Co
ho
rts
-0.0
10.0
1-0
.01
0.0
30.0
00
.91
-0.0
2-0
.21
-0.0
10.0
0-0
.14
-0.0
10.0
20.0
2-0
.02
0.0
00.0
00.0
10.9
0
cx_
up
2C
han
ge i
n %
of
Par
ents
fro
m B
ott
om
40
%,
19
80
-91
Co
ho
rts
-0.0
20.0
10.0
00.0
1-0
.01
0.9
40.0
00.0
3-0
.01
0.0
1-0
.03
0.0
00.0
1-0
.01
0.0
00.0
10.0
20.0
00.9
0
Facto
r p
erc
en
tag
e o
f to
tal v
ari
an
ce
7.2
76.6
25.6
85.4
74.9
34.4
83.9
93.8
73.5
93.2
73.1
12.9
92.9
22.7
92.7
82.7
12.6
12.5
3
Cu
mu
lati
ve p
erc
en
tag
e o
f to
tal v
ari
an
ce
7.2
713.9
19.6
25.0
30.0
34.4
38.4
42.3
45.9
49.2
52.3
55.3
58.2
61.0
63.8
66.5
69.1
71.6
Data
So
urc
es: F
acto
r an
aly
sis
of
data
fro
m N
ati
on
al C
en
ter
for
Ed
ucati
on
al S
tati
sti
cs (
2016).
In
teg
rate
d P
ost
seco
nd
ary
Ed
uca
tio
n S
yst
em
(IP
ED
S),
2014 R
ep
ort
ing
Year,
an
d R
aj C
hett
y, Jo
hn
N. F
ried
man
, E
mm
an
uel S
aez,
Nic
ho
las T
urn
er,
an
d D
an
ny
Yag
an
(2017).
M
ob
ilit
y
Rep
ort
Ca
rds:
T
he R
ole
of C
oll
eg
es
in I
nte
rgen
era
tio
na
l M
ob
ilit
y. S
tan
ford
, C
A /
Cam
bri
dg
e, M
A: S
tan
ford
Un
ivers
ity
/ N
BE
R.
22
higher costs to more ‘distant’ students. Overall, the first dimension captures the historical
legacies and popular stereotypes of private liberal arts educational experiences: the highest-
scoring institutions on Factor I are Amherst, Mount Holyoke, Wellesley, Dartmouth, Wesleyan,
Williams College, Smith College, Trinity College, and Colby College. All of these top ten are in
New England, but other high scores also appear in California (Harvey Mudd, No. 11, Claremont
McKenna, No. 15), New York (Barnard, No. 21, Vassar, No. 22, Colgate, No. 24), Minnesota
(Carleton, No. 27, Macalester, No. 28), and many other states — and note that none of the
regional indicators post significant loadings on Factor I. This first dimension is a space not of
Cartesian coordinates nor Constitutional federalism, but of idealism in the ‘invisible college,’ of
ideology in the Cold-Warrior chemist James B. Conant’s (1956) vision of the “Citadel of
Learning.”
These alternative spatial conceptions highlight the second set of important results: an
institutional educational structure that reflects the historically evolving geographies of ethnic and
racial differences in America. The second factor — and thus the second most important
dimension of common variance in the entire correlation matrix of all forty measures of
institutional characteristics — traces the literal nation-state frontier with Latin America.
Extremely strong loadings for Hispanic student identity (+0.88) and the ‘Outlying Areas’ region
of the U.S. reveal a distinctive geography. Going down the ranked scores of Factor II highlights
the College of the Marshall Islands, the College of Micronesia, Pacific Islands University in
Guam, no fewer than forty-two separate institutions in Puerto Rico, and the University of the
Virgin Islands. Then the list of institutions highlights the emerging Latin American cultural
regions of the continental United States (cf. Haverluk, 1993), with two dozen schools across
Texas, Florida, and California. Scanning down the list draws the fuzzy-set boundaries of
American space, from Texas A&M and UT campuses in Laredo and El Paso, a mixture of public
and private schools in Miami, Los Angeles, and San Diego — and then, farther down the list, the
post-Euclidian Latina/Latino frontiers of diverse campuses in places like Fresno and Bakersfield
in California’s Central Valley, Hackensack and Nutley in Northern New Jersey, and the Bronx in
New York City. Entirely separate dimensions of diversity, however, are involved in two parallel
structures of educational institutions’ Asia-Pacific linkages (Ley, 2010): Factor IV captures the
Asian and multiracial identities of the West Coast (loadings of +0.65, +0.67, and +0.88,
respectively), while another portion of the cross-institutional variation in Asian student identity
loads moderately (+0.41) onto Factor V along with non-resident alien students (+0.61) and the
indicator for Carnegie Doctoral/Research Extensive Universities. The negative loading for the
admissions rate (-0.53) indicates that Factor V reflects the drive for ‘selectivity’ at research
universities competing in the ‘Asia-Pacific Century’ — but the weak loading for
recommendations (0.20599, corresponding to the factor capturing only 4.24 percent of the
variance in the recommendation variable) signifies that this competition entails a wide variety of
means of maintaining selectivity (grades, test scores of various types, and cost — note that the
0.24 Factor V loading for out-of-state/international tuition is the highest positive loading across
all of the factors). Top-scoring institutions on Factor V correspond to popular images of cutting-
edge STEM: MIT, Carnegie Mellon, Columbia, Yale, Rice, Northeastern, Duke, Chicago,
Harvard, Princeton, Caltech, Brown, Washington University in St. Louis, Penn, Brandeis,
Cornell, Stanford, Emory, Tufts, Johns Hopkins ... and so on down the list of brand names that
every journalist, legislator, and taxpayer recognizes from the steady stream of headlines and
university press releases. Factor IV, by contrast, highlights a mixture of less familiar places:
23
three separate campuses of the University of Hawaii, the American University of Health
Sciences just outside Long Beach, California, half a dozen small private schools in Honolulu,
Los Angeles, and Anaheim, San Jose State, San Francisco State, UC-San Diego, UC-Irvine, UC-
Davis, and then several other state campuses in the West (University of Washington at Bothell,
University of Nevada, Las Vegas).
Deeper historical dimensions of American diversity are manifest through the educational
institutions of African American and Native American past and present struggles. Factor IX
posts strong loadings for African American students (+0.62) and a strong locational contrast
separating the nineteenth-century crucible of Northern industrial growth (Great Lakes region, -
0.51) versus enduring Southern class and race structures and segregation (Southeast, +0.85).
Top scores on Factor IX highlight Virginia Union University in Richmond, Lane College in
Jackson, TN, Tougaloo College in Augusta, GA, Rust College in Holly Springs, MS — as well
as the more famous among the HBCs, the Historically Black Colleges: Morehouse, Spellman,
Stillman, Clark Atlanta, and Tuskegee. Nearly all of the schools in the top 50 scores on Factor
IX have student populations at least 80 percent African American, including a number of state
universities (Mississippi Valley State, 91.5 percent, Alabama A&M, 90.7 percent, University of
Arkansas at Pine Bluff, 91.3 percent, Jackson State University, 89.3 percent, and South Carolina
State University, 93.9 percent). Factor XIII, on the other hand, identifies the educational
geographies of the indigenous lands before/beneath the ‘United States’: the strongly positive
loadings for American Indian / Alaska Native students (+0.64) and regional context in the
Southwest (+0.79) highlight a broad mixture of public and private institutions throughout
Oklahoma, Texas, Arizona, and New Mexico. Yet the absolute top-ranked institution on Factor
XIII is beyond the boundaries of the ‘Southwest’ designation, providing a reminder of the
artificiality of all of the regional and state boundaries of America’s manifest-destiny frontier
expansion: the top score is Haskell Indian Nations University in Lawrence, Kansas, where 100
percent of the 808 enrolled students identify as American Indian or Alaska Native. Haskell
attracts students from more than 130 federally recognized tribes, and the institution offers “a rich
cultural heritage which can rejuvenate a student’s Native American pride and heritage,” or
“where a student can learn of their ancestral heritage, sometimes for the first time” (Haskell
Indian Nations University, 2017).
As we consider the contours of American identity, though, we must be very careful with the
technocratic silences of methodological decisions: the variables on student ethnic and racial
identification were defined to enable reliable regression estimates, and to avoid tautological
‘closed number’ biases in PCA/factor analysis (see Davies and Murdie, 1991), and thus the
omitted, reference categories are White, non-Hispanic (for race/ethnicity) Men (for gender).
Sorry, the IPEDS data give us no awareness of the nuance of the sort you would see on our
campus, where the LGBT student club recently organized a ‘Fuck the Cis-tem’ rally to provoke
some conversations on transgender identities. But this old traditional White-male quantitative
categorization becomes crucial in making sense of Factor X, with the strongly negative loading
for female enrollment (-0.71) and the strongly positive loading for theological seminaries and
other faith institutions (+0.79). This factor captures the Judeo-Catholic dimensions of America’s
educational history and present: thirty-five of the top-scoring institutions on Factor X are
Yeshiva schools, Tamudical institutes, or Rabbinical colleges — and all of these are virtually
100% White male enrollment. Presumably most of that is cisgendered White guys. As one reads
24
down the list, more Catholic seminaries and various Protestand-denomination ‘Bible Colleges’
appear, as do somewhat higher percentages of women and somewhat larger shares of non-White
students. 91 of the top 100 institutions on Factor X require letters of recommendation for
admission, a reminder of the massive 23.3 odds ratio for faith institutions in the logistic
regression results (Table 3, Model 3).
A starkly different aspect of ethnoracial difference appears in Factor XV, with its high loadings
for students not reporting information on race/ethnicity (+0.52), Baccalaureate/Associate
institutions (+0.50), and location in New England (+0.75). The list of factor scores seem to
highlight institutions with strong foci on online program — the largest is Southern New
Hampshire University, with total enrollment of more than 43 thousand and more than 200
“career-focused online college degrees and certificates.” A solid majority of SNHU’s students
— 62.9 percent — are racially unknown or ‘invisible,’ given the regulatory framework of civil
rights monitoring and enforcement. U.S. Department of Education regulations require
institutions to ask individual students to identify their race and ethnicity, but for individuals
disclosing this information is voluntary.
A third and final aspect of the factorial ecology is the role of inequality in access and
opportunities for upward mobility. Chetty and colleagues (2017) analyze this issue extensively,
documenting the twin but contradictory realities of opportunity and segregation. On the one
hand, elite universities are powerful engines of upward mobility: among students attending top-
tier universities, students from the lowest-income families have post-graduation earnings that are
only 7.2 percentage points less than the earnings of their alumni peers from the highest-income
families; this gap is 76 percent smaller than the corresponding intergenerational class disparity
for the nation as a whole. On the other hand, very few students from low-income families gain
access to elite schools: children from families in the top 1% of the income distribution are 77
times more likely to go to an “Ivy Plus” institution compared with children from the bottom
20%.12
This is the problem of segregated, stratified opportunity: Chetty et al. (2017, p. 2) go so
far as to conclude, “looking across all colleges, the degree of income segregation across
neighborhoods in the average American city.” This paradoxical combination — relative equality
for a chosen few — obviously adds to the urgency of the question: how do the inequalities in the
landscape of recommendations and institutional access change when we consider measures of
students’ family incomes? The answer, surprisingly, is: not much. If we add six of the most
important measures of access and mobility to the logistic model of recommendation (Table 3,
Model 3), none attain statistical significance even at P<0.15; the strongest effect (at P=0.1615) is
a small reduction in the likelihood of an institution requiring a recommendation, all else constant,
for schools admitting more student from the bottom quintile of the income distribution who go
on to reach to top quintile of earnings in their early adult careers. This finding seems intuitive —
institutions committed to expanding opportunity might be expected to view letters of
recommendation as anti-meritocratic vestiges of connection and privilege. Yet the effect is
small: a standardized odds ratio of 0.898, and a coefficient whose 95 percent confidence interval
crosses unity, from 0.823 to 1.033, implying that the effect could either be negative or positive.
Moreover, shifting the focus from single-outcome logistic causal modeling to the multiple
interdependencies of factorial ecology yields a strange detachment. None of the six income
12
Chetty et al. (2017) define “Ivy Plus” as the eight Ivy League colleges (Brown, Columbia, Cornell, Dartmouth,
Harvard, Penn, Princeton, and Yale) plus the University of Chicago, Stanford, MIT, and Duke.
25
access and mobility measures developed by Chetty et al. (2017) load onto factors with any of the
other three dozen institutional measures. Instead they seem to reveal an entirely separate
underlying dimension of educational variation, posting unambiguous strong loadings on their
own set of factors: Factor VII identifies schools with the highest shares of students from low-
income families; Factor VI captures institutions with the most rapid changes in low-income
representation; and Factor XI identifies those schools presumably able to catapult low-income
students up to the top of the job market. Crucially, while it is common to think of all of these
processes as the same, they are quite clearly separate: Chetty et al. (2017) document, for
example, a sharp decline between the 1980 and 1991 birth cohorts of students from bottom-
quintile families at precisely those colleges that had the highest rates of mobility from bottom to
top quintiles — which “are typically mid-tier public schools” (Chetty et al., 2017, p. 3). Access
remained about the same for the most elite institutions, implying that the restricted access (the
selectivity and exclusivity) associated with top-tier schools may well be drifting down the
hierarchy of institutional competition and austerity. Sifting through the lists of schools with top
scores on these three factors certainly hints at many important stories — especially high rates of
low-income enrollment at places ranging from the Edinboro University of Pennsylvania to
Montana State University, dramatic increases in low-income enrollment at schools like the
College of Mount Saint Vincent in the Bronx, and encouraging upward mobility results at
schools like Edinboro, New Mexico State, Penn State, and Howard University.
Yet these stories frighten me. They scare me because amidst all the logistic regressions and
eigenvalues, all the odds ratios and loadings, it’s hard to see how you and I can do the right thing
when we write our letters of recommendation. As the competition intensifies, we are forced to
write ever more effusive letters. Admissions committees adjust their expectations accordingly.
We adapt to the newly escalated norm, or if we don’t, “our” students fall behind. Every quanta
of passion I devote to writing a detailed letter of recommendation to open doors for one of “my”
students, to try to help them fulfill their hopes and dreams, is a direct assault on the opportunities
available to one of “your” students, or to the great anonymity of an infinity of “other” students.
This is the central dilemma documented yet concealed by my quantitative analysis, as well as the
dry, technocratic findings of Chetty et al.’s (2017) herculean data science quantification of
education:
“Mobility rates are not strongly correlated with differences in the distribution of
college majors, endowments, instructional expenditures, or other institutional
characteristics. This is because the characteristics that correlate positively with
childrens’ earnings outcomes (e.g. selectivity or expenditures) correlate
negatively with access, leading to little or no correlation with mobility rates.”
(Chetty et al., 2017, p. 4).
Read these words a second or third time, because, after all, if we’re going to write a good letter
of recommendation attesting to the quantitative/analytical rigor of one of our students, we would
certainly expect them to read it several times, slowly, carefully. Translation: colleges and
universities can fulfill one of the promises that you often see on their flashy websites and apps,
with all those smiling faces — We can show you the way to success! We can make you a
superstar! — or they can be accessible to low-income people, but they really can’t do both.
Now I look deeper into the detailed output from SAS proc factor. What do all these
26
numbers mean? One of the highest-scoring institutions on the ‘Upward Mobility’ factor is
Howard University, where 1.2 percent of students whose families are in the bottom quintile make
it to the top 1%. This is the top value for this variable in the entire list of institutions. Is this
correct? I go back in and look at the raw data — for some boring methodological reasons I think
the factor analysis was right but applied to the wrong data values — and I see that Chetty et al.’s
(2017) excel worksheet says clearly that for Howard this figure is only 0.09353476 percent. I
think of Ta-Nehisi Coates’ (2015) stories of life on the Howard campus, and of an amazing
young man named Prince Jones, who deserved to make it into the top 1% but never got the
chance to even try. Still, I look elsewhere in Chetty et al.’s (2017) worksheet and I see that even
at Harvard, only 0.4 percent of “children” come from the “bottom quintile and reach Top 1%.”
In my database, Harvard has a zero value for the “cx_utm” variable, which is my name for the
Chetty Upper Tail Mobility Rate. I messed up somewhere. But, um, let’s think about this. The
Chetty data is clear: it says Harvard has a 0.4 percent Upper Tail Mobility Rate. Howard’s rate
is 0.09 percent. They’re both so low. If you come from the bottom of the income distribution
and you go to Harvard and learn to avoid the kinds of data transfer mistakes I just made, you still
only have a 0.4 percent chance — to be pedantic about it, you have a 0.41402927 percent
chance, one out of 241 students — to make it into the top 1%.
One out of 241. Prince Jones got into Howard, not Harvard. Off campus one day, as he was
driving he was followed, chased across the galactic metropolis of suburban highways around the
National Capital region. A cop shot him in the driveway of his girlfriend’s house. Now consider
that quote from a few lines above — ‘correlate negatively with access,’ ‘little or no correlation,’
and so on; that was Chetty et al.’s (2017) evaluation of the bottom quintile to top quintile results.
The truly dramatic inequalities of the past half century involve the top 1 percent compared with
everyone else. Clearly I made some sort of mistake when I match-merged the Chetty variables
— and a careful check reassures me that the problem, whatever it is, is confined to these ‘Chetty
variables.’ But still, when Chetty et al. (2017) analyze the Upper Tail Mobility Rate across all
institutions, here’s what they conclude:
“If we measure ‘success’ in earnings as reaching the top 1% of the income
distribution instead of the top 20%, we find very different patterns. The colleges
that channel the most children from low- or middle-income families to the top 1%
are almost exclusively highly selective institutions, such as UC-Berkeley and the
Ivy-Plus colleges. No college offers an upper-tail (top 1%) success rate
comparable to elite private universities ... while also offering high levels of access
to low-income students.” (Chetty et al., 2017, p. 4).
At this point I think we have reached the limits of a quantitative analysis of letters of
recommendation. There’s an avalanche of data here, and now I can’t be sure if I made a mistake
in importing and match-merging the Chetty data, if they mis-labeled something in the worksheet,
or if I just haven’t read through all the relevant sections of their working paper; the document has
94 pages, and so many tables! I could spend a few more hours, a few more days sifting through
the data, and at some point I probably will. But I did warn you that I wasn’t interested only in
the quantitative stuff.
27
Theorizing Referential Space
Even with the best of data, quantitative analysis can only provide a narrow, limited
understanding of human communication and competition — and in this case our data are far
from the best. In the second part of this analysis, therefore, I shift to a very different mode of
geographical thought and reflection. My purpose here, inspired by what I have learned in the
years since I first read Nelson and Holcomb’s (1989) ‘Referential Treatment,’ is to develop a
theory of ‘referential space.’
To explain what I mean by this phrase, we need to begin with four extended quotes. One pair
looks to the past, while the other describes the present. Consider first a fragment written
sometime around 1858:
“Nature builds no machines, no locomotives, railways, electric telegraphs, self-
acting mules, etc. These are products of human industry, natural material turned
into organs of the human will over nature, or of human participation in nature.
They are organs of the human brain, created by the human hand; the power of
knowledge, objectified. The development of fixed capital indicates to what
degree general social knowledge has become a direct force of production, and to
what degree, hence, the conditions of the process of social life itself have come
under the control of the general intellect and been transformed in accordance with
it. To what degree the powers of social production have been produced, not only
in the form of knowledge, but as immediate organs of social practice, of the real
life process.” (Marx, 1857/1858, p. 706, emphasis in original).
Now consider an excerpt from a lecture delivered in Paris in 1969, exploring the paradox that the
idea of ‘authorship’ is central to the history of Western civilization even when there is no
consensus on how to define an ‘author’:
“Even when an individual has been accepted as an author, we must still ask
whether everything that he wrote, said, or left behind is part of his work. The
problem is both theoretical and technical. When undertaking the publication of
Nietzsche’s works, for example, where should one stop? Surely everything must
be published, but what is ‘everything’? Everything that Nietzsche himself
published, certainly. And what about the rough drafts for his works? Obviously.
The plans for his aphorisms? Yes. The deleted passages and the notes at the
bottom of the page? Yes. What if, within a workbook filled with aphorisms, one
finds a reference, the notation of a meeting or of an address, or a laundry list: is it
a work, or not? Why not? And so on, ad infinitum. How can one define a work
amid the millions of traces left by someone after his death? A theory of the work
does not exist, and the empirical task of those who naively undertake the editing
of works often suffers in the absence of such a theory.” (Foucault, 1969, p. 379).
Now let’s shift our attention to the present day, and the evolving, mundane mechanics of
attempting to write letters:
28
“ Confidential Reference for LTZ39JN7 Jervana Natal.
1. How long and in what capacity have you known the
applicant?
Ms. Natal is a senior due to graduate in May of this coming year. I have known
her for
Confidential Reference for LTZ39JN7 Jervana Natal.
1. How long and in what capacity have you known the
applicant?
I have known Ms. Natal for approximately eight weeks. She is a senior due to
graduate in
Confidential Reference for LTZ39JN7 Jervana Natal.
You have requested technological assistance regarding
the submission of the above-named reference. Please
describe the problem you are experiencing.
The problem I am experiencing is that every time I hit the fucking return key or
try to indent a
Confidential Reference for LTZ39JN7 Jervana Natal.
the fucking return key or try to indent or god forbid fit a coherent sentence into
your fucking
”
(Schumacher, 2014, p. 26)
Finally, we consider an analysis of the politics and poetics of bureaucracies and violence by the
anthropologist David Graeber. In universities in the U.S. and the U.K.,
“...the last thirty years have seen a veritable explosion of the proportion of
working hours spent on administrative paperwork, at the expense of pretty much
everything else. In my own university, for instance, we have not only more
administrative staff than faculty, but the faculty, too, are expected to spend at least
as much time on administrative responsibilities as on teaching and research
combined. This is more or less par for the course for universities worldwide. The
explosion of paperwork, in turn, is a direct result of the introduction of corporate
management techniques, which are always justified as ways of increasing
efficiency, by introducing competition at every level. What these management
techniques invariably end up meaning in practice is that everyone winds up
spending most of their time trying to sell each other things: grant proposals; book
proposals; assessments of our students’ job and grant applications; assessments of
our colleagues; prospectuses for new interdisciplinary majors, institutes,
conference workshops, and universities themselves, which have now become
brands to be marketed to prospective students or contributors. Marketing and PR
thus come to engulf every aspect of university life. The result is a sea of
documents about the fostering of ‘imagination’ and ‘creativity,’ set in an
29
environment that might as well have been designed to strangle any actual
manifestations of imagination and creativity in the cradle.” (Graeber, 2015, p.
134).
Despite or because of their varied lineages, each of these passages helps us to confront the
meanings of recommendations in contemporary academic life. Educational institutions are
expected to prepare individuals to run faster, higher, stronger on the hamster-wheels of a
postindustrial cognitive capitalism in which Marx’s vision of innovation — the power of
knowledge, objectified — has evolved into Richard Florida’s (2003, p. 17) ‘creative age,’ where
“the mind itself becomes the mode of production.” But colleges and universities are themselves
scrambling on the hamster wheels, running furiously to keep up on the rankings and league
tables of overlapping, transnational fields of measurement that purport to deliver various forms
of legitimation (accountability, transparency, efficiency) while refining the technologies of social
rationing (‘world class’ excellence, award-winning distinction). One result is steady growth in a
credential-industrial complex — not just grades and standardized test scores, but all manner of
awards and distinction and letters of recommendation — that reproduces the widening
inequalities of labor in cognitive capitalist production while reinforcing support for the
presumptive fairness of meritocracy (Imbroscio, 2015). Another result is an inescapable
contradiction of loyalties for anyone who provides a recommendation: is our first duty to help a
student gain access to opportunities, or are we obligated to help the institution that is trying to
enhance its prestige by selecting only the ‘best’? Recommendation letters become crucial here in
consolidating what Foucault called the ‘author function’ — the valorization of an individual’s
discourse as worthy of sociocultural, political, and/or economic recognition. Foucault exposed
the contingencies in the meanings of authorship by looking to the past, across the centuries of
Western modernity in philosophy, science, and literature; but his introduction of the problem —
Nietzsche’s meeting notes and laundry lists — is painfully relevant to our advancing cybernetic
present. This is why I’ve included the quote from Julie Schumacher’s uproariously brilliant
Dear Committee Members, a novel about academic life narrated entirely through the text of the
innumerable recommendation letters written by Jason Fitger, a beleaguered professor of
literature and creative writing at an obscure liberal arts college. I’ve lost count of how many
encounters I’ve had with referee management systems like Fitger’s ill-fated attempt to describe
the academic achievements of LTZ39JN7 Jervana Natal, and I wish I could present a
non-fictional sample of digital transcripts or screenshots from the experiences that regularly
bring me to the brink of sledgehammer therapy. But I’m not allowed to do that. In the evolving
legal, regulatory, and quasi-military spaces of online life, Marx’s general intellect and Foucault’s
author function are carefully policed. Every recommendation I submit online is governed by
increasingly detailed ‘Terms of Use’ and ‘End User License Agreements’ (EULAs). As I
struggle to collect a few fragments of uninterrupted moments between emails to compose these
words, an automated request bing lands in my inbox asking for a reference for someone whom
the software identifies with a database-driven middle name: “null.” The system advises me that
“The personal information collected on this form is collected under the authority of the Royal
Charter of 1841, as amended.” How many times, I wonder, has the Charter been amended since
1841? The final screen of the infuriating interface presents a check-box: “I certify that the
above information is correct and accurate. (Checking this box is in lieu of a signature and is
considered legally binding.)” Another request bing arrives. This one includes a ten-point
regulatory outline from Article 27 of the institution’s Doctorate Regulations referring to the
30
“criteria referred to in Article 22, Clause 3, and in Appendix A,” asking me for a letter assessing
whether a candidate’s thesis has achieved a “groundbreaking impact in its field of study” —
whether, that is, the quality “should be ranked in the top 5 percent of the dissertations in the
field” — and specifying that everyone’s identities “shall remain secret.” A few years ago, after a
gruelling session with a horribly unstable interface that spoke to me as Program Referee’s Unique Identifier: 3284-fef5d14a-5790-11e2-
93d6-b59a0448b32, I was presented with a literal Hobson’s Choice: my reference letter for
the student could only be entered into the system if I pressed ‘Accept’ to agree to all twenty-
three clauses of a terms-of-use agreement running 4,817 words. One of these clauses effectively
enmeshed any value in the discourse of my letter into the legal EULA space of a company with a
utopian mission statement — “Our mission is to connect learning to life by matching students to
opportunities across a lifetime of educational decisions” — backed by stern warnings of content
protected “by law including, but not limited to, United States copyright and trademark law, as
well as other state, national, and international laws and regulations.” Hobsons, founded in 1974,
is now part of DMG Information, which describes itself as “a global portfolio of high-growth
business-to-business companies” operating in more than 100 countries. A subsidiary of the
holding company Daily Mail and General Trust, DMG offers seductive language to those
considering investing alonside the distinguished controlling shareholder Jonathan Harmsworth,
4th Viscount Rothermere: DMG describes Hobsons as “the leading provider of tools and
information to help parents and students prepare for and select the right educational destination
as well as helping the academic institution recruit, enroll, manage, and retain their students,”
strategically positioning the firm at the center of “an extremely attractive, large, fragmented and
global sector with long term growth characteristics” (DMG Information, 2017). Now consider
the extensive, quantitative magnitude of referential treatment — between 7.7 and 11.5 million
recommendations every year for college admissions in the U.S. alone, only a small portion of the
vast global flow of recommendations for various stages of education, employment, and all sorts
of other credentials, approvals, and distinctions — alongside the intensive, qualitative meanings
of Marx, Foucault, and Graeber.
This is what I mean by referential space: a large, fragmented, and global sector of cognitive
capitalism in which we are all trying to sell each other students, grants, publications, academic
programs, and ideas themselves. Peer review — the constant flow of recommendations of all
kinds — constitutes this strange, evolving space. It helps to organize the scattered fragments of
achievement and creativity in the millions of traces of authorship that concerned Foucault. It
provides the socially necessary cognitive labor-power to valorize the individual, embodied
tranches of the general intellect that Marx foresaw, translating the power of knowledge
objectified into a revenue stream and investment class. And, perhaps most crucially, it provides
an adaptive cybernetic nexus between the confusing and contradictory spaces of law, regulation,
and social activism in the nodes, networks, and flows of a fragmented global education sector.
Recommendation letters also help us see the limits at the frontiers of competition. As writers, we
all know that when a stellar candidate is locked in competition with other stellar candidates, a
carefully-worded, glowing recommendation can make all the difference in the final decision.
But as readers on committees for admissions, awards, and hires, we’ve all read enough glowing
31
letters packed with words like ‘stellar’ to make our eyes hurt. A discursive arms race generates
escalating expectations for the praise offered in a ‘minimal’ letter of support, creates widespread
suspicion and cynicism, and necessitates entrenched cultures of condescenscion — such as the
esteemed British standard in which letters are ignored unless they include the requisite paragraph
itemizing the candidate’s flaws. The severity of contemporary cynicism is demonstrated by the
anonymous senior professor in the physical sciences at a major research university who writes
for the Chronicle of Higher Education under the pseudonym ‘Female Science Professor’; in a
column on soliciting external letters for tenure and promotion, she warns that comparing a
candidate to “his or her so-called peers” can be a “minefield”:
“I have witnessed several instances in which a letter-writer wrote ‘X is a
spectactularly outstanding pioneering superstar genius just like Z at Other Great
University and I therefore support X 100 percent for tenure at Your University,’
only to hear a committee member say, ‘But I think Z is mediocre.’” (Female
Science Professor, 2014).
I’ve just completed an external tenure review in response to a detailed letter in which the fifth
directive was to provide “a frank appraisal” of “whether she would be ranked among the most
capable and promising scholars in his/her area.” The letter went on to reiterate the demand for
comparison: “It would be particularly helpful to us in our deliberations if you could rate [the
candidate’s] contributions in comparison with others you have known at the same stage of
professional development.”
Most of these forms ask a reviewer to rank a student: Top 1%, Top 5%, Top 10% ... This game
of pin the tail of the bell curve on the student highlights a curious intergenerational dynamic in
grading. In courses with crystal clear distinctions between correct and incorrect answers, it is
theoretically possible for all students to attain perfect scores — or for everyone to fail with
zeroes. In most of the arts and humanities, however, there is no fixed Archimedian point from
which to observe students: their performance is a relative comparison with other students taking
the course in the same place at the same time. If all students could agree to devote nothing more
than modest effort to a course, authorities might be forced to adjust the grading bell curve
downward accordingly. Conversely, intensifying competition and intergenerational progress —
as well as the rising competition among younger faculty pressured to deliver the ‘best’ courses
that ‘challenge’ students to achieve ‘excellence’ — shifts the bell curve upward. No matter how
hard students work when measured as a collective, each individual remains under pressure to
work still harder. Likewise, the competitive processes producing a bell curve are redefined by
the altered ‘time and space’ in which classes occur. The bell curve can now be imposed on
classes and cohorts that are much larger, drawn from a much broader, more diverse and
cosmopolitan world of competitors. The expansion of international recruitment changes the
‘place’ of nations, families, and educational curricula from which each class is assembled, and
‘time’ is transformed when students undertake courses bearing the expectations of being ‘the
best’ to maintain family honor. This creates a transnational, multicultural, and intergenerational
intensification of competition; and so now a growing number of recommendation requests ask
not only for a ranking (“Top 1%”) but a quantification of the denominator as well (“Number of
students to which this student is being compared”).
32
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