Scott Weingart (Carnegie Mellon University) wrote a blog post, reflecting on his research on gender inequality in academia, particularly for his ongoing blog series on acceptances to the annual Digital Humanities conference.
I’ll cut to the chase. My well-intentioned attempts at battling inequality suffer their own sort of bias: by focusing on measurements of inequality, I bias that which is easily measured. It’s not that gender isn’t complex (see Miriam Posner’s wonderful recent keynote on these and related issues), but at least it’s a little easier to measure than race & ethnicity, when all you have available to you is what you can look up on the internet.
Weingart closes his post with a reflection on how his research questions were formed, and the methods he chose to begin answering those questions:
Hopefully this post helps balance all the bias implicit in my fighting for a better world from a data-driven perspective, by suggesting “data-driven” is only one of many valuable perspectives.