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Course Description

Summary

This is a truly interdisciplinary opportunity for students to be part of a real-world project, develop data collection and analysis skills, and learn how to apply them to social problems in the humanities. That racialized and gendered pay gaps plague the arts and publishing, to say nothing of the broader U.S. American labor market, is well known. What is not well documented, however, is the precise price of being a person of color in publishing.  In this course students will work with faculty Mariam Rahmani (Literature) and Michael Corey (Computer Science) to construct, from the ground up, a data-driven study on racial and gender pay gaps in contemporary publishing. First we will learn about the problem, and generally, how to think about it (i.e., by drawing on critical race theory, sociology, etc.). Then we will try to address the issue by closing the public knowledge gap.  We will build out data on pay and author identity using a mixed method approach. Large scale data on pay can be scraped from the web and industry documents. Authors’ self-identifications can be found through their own individual websites, deploying surveys of authors, and analyzing prior interviews. This dataset, once built, can be leveraged to perform social science analyses around discrimination and wage-gap.  Tools used in this class will include python, web-scraping, regression, and data visualization. The product of this research will include written and graphical storytelling to illuminate the gaps in pay based on authors’ racial and gender identities, or as close as we can get (a.k.a, their analyzable “ascribed characteristics”).