We would like to be clear about the important role of terminology in this work, specifically the distinction between sex, gender identity and gender expression, and the non-binary nature of each of these. In discussing our data we refer to the differences we observe as gender differences. This is in contrast with sex differences, which we do not believe our data speak to. That is, the observed differences in outcomes of men and women in linguistics are, under our assumptions, due to social factors and not biological factors. To be specific, given our data collection practices, we interpret the gender expression of individuals based on their photos and first names, and we believe that this perceived gender correlates (albeit imperfectly) with gender identity. This means we are unable to comment on the experiences of trans and non-binary individuals. Furthermore, we believe that the interpretation of these modes of gender expression is responsible for others’ inferences about sex and gender and it is these inferences that motivate the different treatment that men and women experience. We hope for future research to explore these questions more adequately.
We refer the reader to this link for further information regarding the difference between gender identity and sex.
We believe that bias, recognized or not, manifests in specific actions which result in patterns of discrimination. The observations we make on this website are based on our own data, and care should be taken in extrapolating the results to the whole field.
We do not quantify the actions which cause the pattern we see of lower proportions of women in each stage of the academic career, though we hope that our investigation of publication rates is a move toward this. Given other research on bias in academia, however, we can speculate on what causes these imbalances. For example, women in academia are disproportionately burdened with professional service duties ([Guarino and Borden, 2017][guarino-borden-2017) and housework/childcare (Mason, Goulden, and Wolfinger, 2006); don’t benefit from male privilege in abstract reviews (Roberts & Verhoef, 2016); receive poorer student evaluations (Mengel, Sauermann, and Zolitz, 2019); are written poorer letters of recommendation (Trix & Pskena, 2003], Madera, Hebl and Martin, 2009); and experience outright misogyny such as the sexual harassment and assault. All of these are manifestations of implicit gender biases and potentially contribute to the attrition rates that we see, as well as any differences we see in publication rates. Because we are not directly measuring the above actions, only the symptom, we urge the readers to be careful in interpreting our results. However, our data is useful for providing a before/after metric to determine if interventions have the desired effect.