We conceptualize and measure two forms of gendered knowledge in research language: gender referents (explicit references to sex and gender) and gender-associated terms (words that are implicitly associated with women or men researchers). • We analyze gendered knowledge in nearly one million dissertations published in U.S. institutions (1980–2010) with natural language processing techniques. • We find explicit references to women and females in dissertations increase chances of becoming a faculty advisor. • In contrast, doing research traditionally associated with women reduces chances of becoming a faculty advisor. • Our study implies implicit gender bias in the evaluation of research while modest progress in the valuation of research explicitly studying women and females. 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.
