In the interest of remaining accountable to the values statement for this book, the authors tasked me with performing an audit of all the individuals, projects, and organizations referenced in Data Feminism. Quantifying these references provided important information about which perspectives were being included in the work and to what extent. At the same time, this process presented difficult-to-answer questions about identification and classification. Therefore, this methods statement will serve to explain how we approached those questions and what our answers were. First, we had to decide what would constitute a "reference" that needed to be recorded in the audit. Every individual mentioned by name was counted as a single reference , as was every project (i.e., Kiln's Ship Map). Corporations were mostly excluded from the audit unless they played an active role in an example and were mentioned more than once. For instance, Target is included because its pregnancy-targeted marketing strategy is used as a major example in chapter 1. On the other hand, Instagram is not included despite being mentioned multiple times in the Serena Williams example because in that case the social medium is merely the site where Williams and her fans exchanged stories of their birth experiences. Each categorization then required its own investigation. For individuals, we attempted to verify their race, gender, country of origin (which fed into our Global South/Global North distinction), and indigeneity. Additional categorizations included whether or not references were from within the academy and if they represented an example of good data practices or "what not to do." References were logged as "com-munal" if they were community-driven (e.g., Data for Black Lives), and a separate category recorded whether the reference provided a "nonvisual example" of data work or not. Each record was further classified by importance. One reference constituted "passing" importance; two to four references, "more than once;" and beyond that, "central."
CITATION STYLE
D’Ignazio, C., & Klein, L. F. (2020). Auditing Data Feminism, by Isabel Carter. In Data Feminism (pp. 223–224). The MIT Press. https://doi.org/10.7551/mitpress/11805.003.0012
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