Lewis, M. & Lupyan, G. (2020). Gender stereotypes are reflected in the distributional structure of 25 languages. Nature Human Behavior. [preprint] [supplemental app]
In this project, we use word embedding models to measure bias in the distributional statistics of 25 languages and find that languages with larger biases tend to have speakers with larger implicit biases (N = 656,636). These biases are further related to the extent that languages mark gender in their lexical forms (e.g., “waiter”/“waitress”) hinting that linguistic biases may be causally related to biases shown in people's implicit judgments.
The file writeup/journal/iat_lang.Rmd contains the code for the finsal set of analyses reported in the paper. The analysis directory contains all scripts used to pre-process the data before analysis. The data directory contains all the relevant data. An interactive version of the Supplemental Materials to the paper can be found at https://mollylewis.shinyapps.io/iatlang_SI/, and in the repository at paper/journal/SI/.
The above figure (Fig. 2 in the paper) suggests that languages with more gender-biased language statistics tend to have speakers with greater gender bias.
Feel free to email me with questions and comments at [email protected].