【台大社會系演講系列】
主講者：Lanu Kim（Assistant Professor, Korea Advanced Institute ）
題目：Gendered Knowledge in Fields and Academic Careers
主持人：呂青湖（臺灣大學社會學系副教授）
時間：2022年 4 月 7 日（四）12:30-14:00
地點：視訊會議
＊本演講為視訊會議，參與者請於當天至台大社會系臉書專頁取得直播連結（https://www.facebook.com/ntusociology）
演講介紹：
Women and men often contribute differently to research knowledge. Do differences in these contributions partially explain disparities in academic career outcomes? We explore this by looking at how gender is embodied in research language, and then ascertain whether the adoption of more gendered research language affects career outcomes beyond the researcher's attributes. We identify different forms of gendered knowledge—gender referents (explicit references to sex and gender) and gender-associated terms (words that are implicitly associated with women or men researchers)—by applying natural language processing techniques to nearly one million doctoral dissertations published in the United States between 1980 and 2010. We then determine whether employing gender referents and gender-associated terms affects the course of PhDs’ ensuing careers. We find women researchers have lower chances of securing academic positions than men in every field; explicit references to women as research subjects are modestly rewarded in comparison to references to men; and more career opportunities are afforded to research knowledge associated with men. These results suggest that academia is slowly correcting the traditional and explicit bias of studying men at the exclusion of women. Still, there remains a stronger implicit bias against knowledge associated with women scholars. We discuss relative differences between humanities and social sciences versus natural sciences, technology, engineering, and math, as well as potential treatments for offsetting bias in those fields.
講者介紹：
Lanu Kim is an assistant professor in the school of humanities and social sciences and a joint professor in the school of computing, Korea Advanced Institute of Science and Technology (KAIST). After finishing her sociology PhD at the University of Washington, she was a postdoctoral fellow and data science scholar at Stanford University. Her research broadly contributes to the theoretical understanding of academic knowledge creation by mainly examining the impact of academic search engines, gender inequality in higher education, and the social structure of knowledge construction. To investigate, she utilizes new big data sources, innovative analytical strategies, natural language processing, and advanced statistical methods and work with interdisciplinary research teams.