The results we present here are only a first step towards a data-based approach to understanding bias in Linguistics. Ultimately, we would like to use such results to come up with meaningful solutions for the issue.
We have been busy growing the network of people working on this project. In the early stages of the project, the work was presented at Michigan State University’s Undergraduate Research and Arts Forum 2017 and at the Michigan State Undergraduate Linguistics Conference 2017.
In the past few months, we presented and lead workshops at UMD’s Winter Storm 2018 and formed a working group made up of graduate students and faculty involved in UMD’s Language Science Center. We are presenting at the LSC’s lunch talk series, and are working to connect with other like-minded groups addressing gender and other biases in the field of linguistics.
Broadly, these are our big goals for this project:
- Conduct further descriptive work to identify where and why the bias exists, including looking at things like race, native language, sexual orientation, etc.
- Collectively come up with solutions to such problems of bias. We are particularly interested in working with departments and institutions to think about the policy implications of our data.
Here are some specific goals we are currently addressing:
- Are there differences in publishing rates? These can be counted in various ways: by number of publications, by first/last authorships, or by single-authored papers.
- Do individual journals show gender biases? This may manifest in the amount of time it takes to get an article published or the proportion of acceptances vs. rejections, and is particularly important with regard to reviewer blindness.
- Do women or men publish more at similar stages in their career, particularly as graduate students?
- Are there disparities in the proportion of men/women in the audience vs. the proportion who ask questions? Work on this has been undertaken by several linguists (for example, here and here), so we are interested in creating an online app where people can go and enter in their own data to have it included in a database. The results will be presented on the webpage much like our current information is, with plots and data available for download.
- What are the differences in salaries at similar stages of the career at public institutions?
- What policies may mitigate the leaky pipeline (for example, regarding parental leave)? How can we measure the effects? How can we encourage schools to adopt these policies?
- Publicizing our results more!