Distraction Free Reading

Let’s Think about the University: Anthropology, Data Science, and the Function of Critique

There have been surprisingly few sustained, ethnographic studies of the university that aim to understand it as an institution devoted at once to the production of knowledge and technologies, the circulation of those products, and the cultivation of particular types of subjects. Ethnographers have largely worked at it piecemeal, with admittedly excellent work from both the anthropology of education and of science carving out various areas of inquiry: classrooms, laboratories, admissions offices, student groups, start-up incubators. To my mind, it seems that the lack of a synthetic approach to the knowledge work going on in the university might be due to the disappointing fact that these two camps within anthropology don’t talk to each other very much. In part, this is a result of their different goals, positions within the ecology of anthropological knowledge production, possible sources of research funding, and available career paths both within and without academia; yet, despite the sociological intelligibility of this lack of communication, it remains intellectually unfortunate.

As the business of research and education becomes increasingly corporatized, increasingly shaped by wider forms of rationality that rely upon quantification, standardization, and the devolution of responsibility to the individual, it becomes correspondingly urgent to develop a rigorous, holistic understanding of the university as such. This has only been underscored by my fieldwork among Russian data scientists, who are themselves involved in the ongoing reorganization of higher education here. That is to say, the neoliberal university qua institution, with its own internal forms of organization and expertise as well as its place within the broader political economy, deserves to be the object of a newly shared inquiry. The current shape of the university has profound implications for the professional lives of anthropologists of both science and education, and similarly thorough-going epistemological consequences for their ongoing, ultimately complementary attempts to understand how contemporary people make knowledge.

I’m working through the latter half of this proposition in my current research project. Data science has emerged as a key site of intervention into the educational system in Russia; elites from both industry and academy are working together to modernize and re-purpose Russia’s formidable pedagogical infrastructure in pure mathematics and theoretical computer science to train a new generation of algorithmists, developers, and programmers in both the practical skills and professional attitudes that they see as necessary for the creation of a truly Russian knowledge economy. The result has been both the creation of a number of hybrid, industrial-academic institutions and wide-ranging modifications to curriculum and requirements at more traditional institutions. These changes are occurring within a broader context of profound reforms to post-graduate education1 and the science system more generally.2

The "Golden Brain" of the Praesidium of the Russian Academy of Sciences.

Image: The “Golden Brain” of the Presidium of the Russian Academy of Sciences. Photo Credit Nataliya Sadovskaya. Licensed for re-use under Creative Commons Attribution 2.5.

One critical component of these broader changes has been the call for new forms of accountability in education and research. To an extent, these echo existing developments in America and Western Europe: the tying of academic salaries to impact factors of publications; the auditing of pedagogical success; and the building of whole metrologies of scholarship. Indeed, many writing on audit culture3 and the neoliberal university4 are keen to point to scientometrics, with its mathematization of scholarly life, for managerial encroachment on academic freedoms in the name of rationalizing the university. To be sure, the computer scientists, sociologists, and mathematicians working in the data sciences that I have been talking with in Russia are near-ubiquitously invested in the project of “rationalizing” academic life, and many are actively working to forge new tools to measure academic output, trace academic networks, and produce metrics for evaluating novel pedagogical techniques.

We anthropologists, however, should not think that we have a unique purchase on self-reflexivity, on understanding our own implication as researchers and professional educators in such processes of reorganization. On the contrary, my time here has continually reinforced just how acutely aware data scientists are of the recursive relationship between organizational structures and disciplinary practices. While there is undoubtedly truth to the claims of their detractors, I would like to suggest that these processes take on a slightly different valence in the post-Soviet science system: they are viewed as a necessary, indeed revolutionary tool for the dismantling of entrenched bureaucracies and restructuring of unarguably outdated educational and professional practices. The people among whom I have been conducting research know full well that their work has immediate, concrete implications for themselves and their colleagues, and I have been struck by the decidedly “humanistic” understandings of teaching and research that underlie their attempts to build consensus about how best to measure their achievements. Though they are keen to use a quantitative cudgel to chase out the bad old days of “irrational” and “outmoded” Soviet bureaucracy, they are cognizant of the subjective and political character of their quantifications. As such, the conversations about scientometrics or the analysis of scholarly networks that I have participated in here have been characterized as much by thoughtful, philosophically-minded discussions about the nature of research, teaching, and collaboration as they have been by discussion of technical details; they are, at least in part, conversations about the future of the Russian university.

My colleagues here are also, however, aware that they are working in computer science and applied mathematics, global, technologically sophisticated disciplines that “move fast” — what we might be inclined to call eminently timely disciplines — and that their techniques and metrics must be up to the project of being contemporary if they are not to remain merely intellectual exercises. We anthropologists, at least those of us with tenure or training at elite institutions, have been privileged with the space to indulge in untimely speculation and criticism5 by our place within the disciplinary landscape of the contemporary university; for my interlocutors, however, questions of reform pose existential problems. Their jobs and the future of their fledging discipline depend in no small part upon their ability to leverage their own disciplinary expertise to prove the usefulness and success of their projects within a science system notoriously resistant to organizational innovation.

Untimeliness has its place, and in evaluating these trends we should not get carried away with a love of the new. We should keep in mind that neoliberalism itself is a “revolutionary” movement, and that one of the key features of neoliberal governmentality is the proliferation of tools and techniques of freedom. However, one does not have to be a particularly sophisticated reader of Foucault or Luhmann to realize that our anthropological criticism and principled resistance to metrics might also be structural functions of this same governmentality. Our own untimely sentimentality might be an essentially conservative product of our position within the self-same political economy as our object of criticism. Far more serious thinkers6 than I have spent a long time7 trying to work their way8 out of this rather unpleasant9 realization, with little unalloyed success. I would like to suggest, however, that there might be something to take seriously in my data scientist colleagues’ attempts to use their own professional expertise to intervene into the university itself, not in the name of maximizing profits but in order to make it a more livable and workable institution. To ignore the fundamental honesty of either their epistemological practices or political investments here would be the worst form of anthropological condescension.


1. Dezhina, I. 2014. “Russia’s Academy of Sciences’ Reform: Causes and Consequences for Russian Science.” Russie.Nei.Visions 77.

2. Klyukanova, A. and Chiappa, R. 2014. “Doctoral Education in Russia: Reforms and Pending Challenges.” CIRGE blog. Retrieved 31 March 2015.

3. See, for example, Wright, S. 2014. “Knowledge that Counts: Point Systems and the Governance of Danish Universities,” in Under New Public Management: Institutional Ethnographies of Changing Front-Line Work. D. Smith and A. Griffith, eds. Toronto: University of Toronto Press, 294-338.

4. See, for example, Shore, C. 2008. “Audit culture and Illiberal Governance: Universities and the Politics of Accountability.” Anthropological Theory, 8 (3): 278-299.

5. Rabinow, P. 2007. Marking Time: On the Anthropology of the Contemporary. Princeton, NJ: Princeton University Press.

6. Latour, B. 2004. “Why Has Critique Run out of Steam? From Matters of Fact to Matters of Concern.” Critical Inquiry 30: 225-248.

7. Readings, B. 1997. The University in Ruins. Cambridge, MA: Harvard University Press.

8. Westbrook, D. 2008. Navigators of the Contemporary: Why Ethnography Matters. Chicago: University of Chicago Press.

9. Deleuze, G. and Foucault, M. 1977. “Intellectuals and Power.” In Language, Counter-Memory, Practice: Selected Essays and Interviews, D. Bouchard. ed. Ithaca: Cornell University Press, 205-217.

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