A rise of academic capitalism over the past four decades has been well documented within many research-intensive universities. Largely missing, however, are in-depth studies of how particularly situated academic groups manage the uncertainties that come with intermittent and fickle commercial funding streams in their daily research practice and problem choice. To capture the strategies scientists adopt under these conditions, this article provides an ethnographically detailed (and true) story about how a single project in Artificial Intelligence grew over several years from a peripheral idea to the very center of an academic lab’s commercial portfolio. The analysis theorizes an epistemic form—nimble knowledge production—and documents three of its lab-level features: 1) rapid prototyping to keep sunk costs low, 2) shared search for “real world problems” rather than “theoretical” ones, and 3) nimble commitment to research problem choice. While similar forms of academic knowledge transfer have been lauded as “mode 2,” “innovative,” or “hybrid” for initiating cross-institutional collaboration and pushing science beyond disciplinary silos, this case suggests it can rely on fleeting attention to problems resistant to a quick fix.
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There are surprisingly few exceptions to this generalization. Most notable is Kleinman’s (2003) ethnography of a plant pathology lab. Even science and technology studies (STS) scholars, for example, who tend to frame their intervention around bench science, still rely heavily on interviews, surveys, and archives to document day-to-day decision making.
Epistemic form refers to the “suite of concepts, methods, measures, and interpretations that shapes the ways in which actors produce knowledge and ignorance in their professional/intellectual fields of practice” (Kleinman and Suryanarayanan 2013, p. 492). This practice-based usage of the concept rejoins the philosophical cleavage offered by Collins and Ferguson (1993) between “epistemic form” (“target structures that guide inquiry”) and “epistemic strategy” (“general purpose strategies for analyzing phenomena”). My use of the concept focuses on portable and “trans-situational” strategies of decision making as “structures that guide inquiry,” rather than conceiving of social structure as somehow existing at a level above, below, or beyond empirical activity.
For a familiar example, consider Platt’s (1995) summary of the emphasis of the so-called Second Chicago School of Sociology: “A strong emphasis on the importance of going out and getting data—an important continuity with prewar tradition—and a relative lack of interest in more abstract theoretical issues” (p. 102).
The names of the lab, research projects, and members are all pseudonyms.
This is an example of how a proprietary technology of a private, for-profit company structures methodological technique within academic research, similar to how Kleinman (2003) documented the use of Taq polymerase, owned by the drug company Hoffmann LaRoche, in plant pathology science.
Semantics is a sticky problem with a long history in the philosophical debates within AI. The most well-known formulation comes in John Searle’s (1984, 1980) infamous “Chinese room” argument, a thought experiment in which an English-speaking man is locked inside a room with commands written in Chinese characters and a book of instructions. The man is able to produce proper responses to the commands by manipulating Chinese symbols but without acquiring any understanding of the language or what either the commands (inputs) or results (outputs) mean. Nonetheless, this man-in-a-box would likely fool people outside the room into thinking he understands Chinese. This problem shares a family resemblance to Collins and Kusch (1998) distinction between “polymorphic” and “mimeographic” actions, with only the former characteristic of human-level intelligence whereas machines excel at the latter. For a more general history of attempts to solve the problem of semantics in AI, see Crevier (1993, pp. 269-271).
Paper publication in academic AI and most of computer science often comes in the form of conference proceedings, which reflects a technical and rapid change knowledge domain.
CML members did not discuss issues of a digital divide, authorial intentionality, geographic representation, access, deception, or any other of the myriad ways that the notion that blogs represent an unvarnished window onto contemporary culture might be problematized.
CML members used the term “wrapper” in a colloquial manner to refer to building code that enabled a general-purpose program to be designed for a specific application. Technically, a “wrapper function” involves adapting an existing library of code to a different interface.
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Data collection was financially supported by the MacArthur Foundation, the Mellon Foundation, the Kaplan Center for the Humanities, and the Department of Sociology at Northwestern University. The write up was supported by the Humanities Institute and the Office of the Vice President for Research at the University at Buffalo, SUNY. The Sociology Department at the University of Toronto has been an inspired tribe to join while finishing it. “Sandra,” “Ammon,” “Charles,” and “Cliff” were generous with their time, patience, and general good will. Against reason, the following folks encouraged this article’s own story of fits and starts: Jorge Arditi, Zaheer Baber, Ellen Berrey, Jim Bono, Alan Czaplicki, Jim Davis, Mike Farrell, Jerry Flores, Jaume Franquesa, Jordan Geiger, Bob Granfield, Hanna Grol-Prokopczyk, Carol Heimer, David Herzberg, Jonathan Katz, Fred Klaits, Daniel Lee Kleinman, Anna Korteweg, Jennifer Light, Adam Malka, Terry McDonnell, Dalia Muller, Libby Otto, Miriam Paeslack, Ewa Plonowska Ziarek, Elizabeth Popp Berman, Katja Praznik, Mike Sauder, Erik Seeman, Camilo Trumper, and Marion Werner. I dedicate this one to my mentor, Art Stinchcombe, a genius at recognizing the interesting bits.
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Hoffman, S.G. A story of nimble knowledge production in an era of academic capitalism. Theor Soc 50, 541–575 (2021). https://doi.org/10.1007/s11186-020-09422-0
- Academic capitalism
- Artificial intelligence
- Epistemic form
- Knowledge production
- Problem choice
- Research policy