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 (read more...)