A University of Minnesota professor has developed an online tool that allows her peers a chance to check their syllabi to see if the authors of assigned readings are balanced by gender and race.
Professors can upload their syllabi to the Gender Balance Assessment Tool website to receive an approximate percentage of authors who are women, as well as an approximate breakdown of authors’ races, such as Asian, Black, Hispanic and white.
“Women are cited less often than men, and are also underrepresented in syllabi. Yet even well-meaning scholars may find that they have difficulty assessing how gender-balanced their bibliographies and syllabi really are,” the website states. “Counting is tedious and prone to human error, and scholars may not know the gender identities of all the authors they cite. This tool aims to help with that, by automating the process of evaluating the (probabilistic) gender of each name and then providing an estimate of what percentage of the authors on a syllabus are women.”
Its creator, Jane Sumner, is an assistant professor of political science at the University of Minnesota. She said she created the tool because she noticed too often in academia that syllabi are not diverse enough regarding the gender and race of authors of listed readings and she hopes professors will use her website to help diversify their syllabi.
“The basic idea is that a lot of people were interested in the idea of having syllabuses that were more diverse in terms of who the authors were,” Sumner told the Minnesota Daily.
Neither Sumner nor the University of Minnesota responded to The College Fix’s requests for comment.
Sumner told the Daily she began to think about the diversity of authors in graduate school when she noticed that white males authored most of the material in her classes.
The Gender Balance Assessment Tool does not actually know the gender nor race of authors when syllabi are uploaded by professors, rather it simply assumes one’s gender and race based on their name only, according to the website. It adds that it does not work well with those who identify as non-binary.
“Because this algorithm works by assigning a probability distribution to binary genders based on names, this tool does not function well to accommodate non-binary gender identities,” the site states.
The racial prediction side of the tool also makes educated guesses, and it encourages the user to exercise caution with the percentages it gives. This is because they can be largely inaccurate as the site does not actually know the author, and uses a racial prediction based on surnames.
“Resultant prediction are the average of the racial predictions for all surnames that can be predicted. I encourage caution in interpreting the racial balance numbers, as these are both less accurate and less precise than the gender balance estimates,” the website states.