Learning about gender bias in Wikipedia articles

Samantha Kao is a graduate student at Western Washington University and a member of the Association for Women in Mathematics. She recently completed our professional development course as a Wikipedia Fellow, in which she joined linguists, psychologists, and chemists in improving Wikipedia’s coverage of a diverse range of scientific and mathematical topics. Here, she shares her take-aways.

Samantha Kao, graduate student, member of AWM, and Wikipedia Fellow.

I heard of the Wikipedia Fellows program through an email from the Association for Women in Mathematics (AWM).  The sentence that caught my attention stated “We believe scholars who bring their understanding of complex mathematical topics and women in math to Wikipedia can empower the public to participate fully as citizens.”  As someone who frequently uses Wikipedia, I wanted to use my subject area knowledge to support that resource in whatever manner I could. Further, the interdisciplinary approach to public information appealed to me. Before realizing I wanted to study math, I had majored in International Studies and worked in the environmental field and community college education.  

After applying, I received an invitation to join the cohort focusing on Communicating Science, which included linguists, psychologists, and chemists. I enjoyed speaking with other academics about how to better communicate parts of my field and how to use academic writing in such a public realm. During my first week, I learned two astonishing facts that I would repeat nearly every time I told someone about my participation in this program: roughly 80-85% of Wikipedia editors are white males and only about 17% of Wikipedia biographies feature women.  I immediately knew I would want to focus on biographies of female mathematicians.

After an introduction to the program and the mechanics of editing in Wikipedia, the bulk of the program focused on improving articles related to our academic field.  We spent several weeks selecting an article in Wikipedia, assessing it for things it did well, and pinpointing areas for improvement. This first article for our editing experience would ideally need significant editing.  I chose to begin with Julia Robinson, whose work was instrumental in solving Hilbert’s Tenth Problem. As the program continued, my outlook turned to smaller edits on specific portions of an article, rather than a wholescale renovation.  I realized I didn’t bear responsibility for the entire article’s credibility, and this realization prompted a looser framework of what I chose to edit.

Having chosen to focus on female mathematicians, I came across several helpful resources for the editing process:

The Finkbeiner test, analogous to the Bechdel test for cinema, was one of the most interesting resources I found.  It proposes several questions to assess sexism in media coverage, aiming to eliminate the tendency to treat a “subject’s sex as her most defining detail.”  

In my past experiences, if an article presented personal information matter-of-factly, I rarely questioned whether the information needed to be there in the first place.  After all, the source’s writer chose to include it, and I trusted the source (therefore the writer as well). I didn’t see a problem with being told that a woman was divorced or why, and I hardly ever wondered why I wasn’t given marital status or similar information regarding a male in the same field.  Once I started perusing Wikipedia with an editor’s lens, I became much more aware of the inherent bias in many articles featuring female subjects. I never saw outright sexism such as describing a subject’s excellence as a caretaker more than her mathematical contributions; rather, it was far more common to see things like “she fell in love and married A. Persyn” or “she then moved with her husband for his job.”  

Upon considering this bias, another realization developed during my editing experience. I assumed gender bias was solely fault of the Wikipedia editors, so I elected to double check the cited sources and do a bit of my own research on sources.  Nevertheless, I found that many of the sources I reviewed had the same information on the Wikipedia article and not much further. In this sense, perhaps much of the gender bias in Wikipedia stems from original bias in the primary sources, which are summarized by that Wikipedia article.  So what can we do as editors to change this? It’s difficult to find further information about a mathematician who passed away forty or fifty years ago and whose work took place without internet documentation. Of course I don’t know how to solve this situation, but it seems to me that having more and better examples of appropriate articles for female scientists certainly can’t hurt.  In this sense, editing and improving current Wikipedia articles sets a better standard for what Wikipedia as a public encyclopedia should look like.

Returning to my first experience editing, I want to bring up a quote from a piece by Julia Robinson’s sister in an AMS newsletter (Vol. 43:12, December 1996):

“‘I am appalled at the prospect of details of my life and beliefs appearing in print. (I don’t even want to be written about after I’m dead but that is difficult to manage.)….’ In [Robinson’s] view a mathematician was his or her work; personality/personal details could do nothing to illuminate that and so were of no importance. She detested what she saw as the cult of personality: the prying into every aspect of what was private that was and still is prevalent in biographical—and, for that matter, autobiographical—writing.”  (pp. 1490-1491)

For the most part, my experience with trying to eliminate gender bias on Wikipedia has dealt only with biographies of mathematicians.  From what I’ve seen, it’s less the need for deletion of irrelevant information (which is still frequently needed), but more balanced information based on what a mathematician’s biography truly should feature.  Further, my guiding principal has become the idea of improvement rather than perfection. Any improvement leaves an article better than it was before.


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