About the event

Abstract

Informal workplace networks serve as the fundamental mechanisms through which social closure operates and group-based socioeconomic inequalities are reproduced. Yet real-time, large-sample data of such interactions are notoriously scarce, largely because private workspaces are, well, private. Yet occasionally, informal interpersonal connections in the workplace may become visible in public settings—at least among one group of professionals: academics! Academic conferences are key sites for building the kinds of professional networks that shape scholarly careers. Yet, it is hardly surprising that these informal interactions are often deeply segregated by subgroups such as race, gender, and seniority. In this study, we introduce image data and computer vision models as a novel approach to understanding these interactions. First, we collect thousands of photographs of informal interaction groups at sixteen academic conferences organized by major professional associations in the United States. In the second step, using both human coders and a locally fine-tuned open-source vision–language model, we identify interaction groups and code individual-level characteristics—gender, race, and age—in the images. Third, we analyze the data to quantify group segregation and network hierarchy both within and across different conferences. Our findings challenge the notion that conferences are spaces where scholars randomly mingle. Across conferences, informal interactions are strongly segregated along racial, gender, and age lines. The level of racial segregation in these face-to-face encounters is substantially higher than that observed in more stable and structured settings such as coauthorship. Informal interactions also function as a mechanism that sustains inequality in network positions across groups: Mid-career white scholars tend to occupy the most central positions in interaction networks, conferring a status advantage relative to minority groups.