Jeff Pooley (Annenberg)

Jeff will discuss the implications of AI summary and literature recommendation for academic reputation and knowledge equity. An avalanche of tools from both AI companies and the big-five commercial scholarly publishers reply to user prompts with summarized answers, drawing on and often citing the scholarly literature. At the core of these tools is a series of verbs: to surface, to rank, to summarize, and to recommend. In this sense the AI products are already acting as knowledge arbitrators, by picking winners and losers according to what they make visible. There are two big and interlocking problems with this role: The models are trained on the past, and their filtering logic is inscrutable. As a result, they may smuggle in the many biases that mark the history of scholarship, around gender, geography, and other lines of difference. In this context it’s useful to revive an old concept in the sociology of science. According to the Matthew Effect—named by Robert Merton decades ago—prominent and well-cited scholars tend to receive still more prominence and citations. The flip side is that less-cited scholars tend to slip into further obscurity over time. These dynamics of cumulative advantage have, in practice, served to amplify the knowledge system’s patterned inequalities—for example, in the case of gender and twentieth-century scholarship, aptly labeled the Matilda Effect by Margaret Rossiter. Jeff’s presentation will discuss how the wide uptake of AI tools in scholarship, especially proprietary ones, may produce a Matthew Effect on the scale of Scopus, and with no paper trail.

Jeff Pooley is lecturer and research associate at the Annenberg School for Communication, and co-directs mediastudies.press, a nonprofit open access publisher in communication and media studies. He writes frequently on open access and related issues in scholarly publishing. His other research interests center on the history of media research within the context of the social sciences, with special focus on the early Cold War behavioral sciences.