講題：College Rankings and Socioeconomic Self-Sorting in Higher Education
講者：James Y. Chu （Assistant Professor of Sociology, Columbia University）
主持人：蘇國賢（台大社會系教授）
時間：2025/5/16（五） 12:30-14:00（前三十位入場報到聽眾可領取餐盒一份，敬請準時入座）
地點：台大社會與社工系館R319
演講語言：英文
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演講說明：
Formal rankings arise from algorithms that incorporate multiple inputs into single metrics, and a recurring criticism is that they impose excessive uniformity in evaluations. A less explored possibility is that decontextualized metrics encourage greater heterogeneity in the inferences that users draw. Across three experiments, I investigate the inequality implications of this possibility in the context of educational rankings. Study 1 shows that rankings are perceived as stronger signals of exclusivity, academic rigor, and beneficial social ties for those from higher socioeconomic status (SES) backgrounds, while serving as stronger signals of exclusion (i.e. unwelcoming) for those from lower SES backgrounds. Study 2 demonstrates that the introduction of numeric rankings exacerbates – and not only merely reflects – these differences in interpretation. Study 3 shows that inconsistent decoding can be inaccurate, as when adolescents whose parents lack a college degree incorrectly perceive rankings as a significantly stronger signal for the net price of a college than their more advantaged peers. Across these studies, these SES differences in what people infer from rank explain self-sorting, where those from higher SES backgrounds prefer prestigious colleges significantly more than those from lower SES backgrounds.
講者介紹：
James Y. Chu
Hi! I’m an Assistant Professor of Sociology at Columbia University. I am particularly interested in two key topics: (1) how status hierarchies are formed and formalized, and their attendant consequences for social inequality; and (2) how societies can reduce extreme intergroup conflict, with emphasis on partisan animosity in the contemporary United States.
I tend to write papers that develop and test middle-range theories where propositions are formally defined, logically coherent, and avoid circularity. I try to draw on the best possible methods and data to either develop new theory or test key predictions. This commitment explains the diversity of methods and data sources deployed across my research – including survey and field experiments, social network analysis, and ethnography. I often pair complementary methods, including case studies with a representative survey; social network methods with agent-based models; or ethnography with text analysis.
You can find my work in journals like American Sociological Review, American Journal of Sociology, Social Forces, Administrative Science Quarterly, Science, Proceedings of the National Academy of Sciences, and Journal of Labor Economics.
