Chapter 4 of the book Data Feminism by Catherine D’Ignazio and Lauren Klein is centered around the principle of rethinking binaries and hierarchies. They ground their understanding through an analysis of the gender binary in data collection which determines who gets counted when considering policymaking and resource allocation and who remains invisible. Using gender as the subject of analysis and scrutiny throughout the chapter is a generative one given the reliance on gender-based classifications — for something as small as vitamins to something as big as the roles within a family, — the fluidity of gender, and its heterogeneity. Gender can be seen as a ‘canon’ in relation to conversations around binaries. Disclosing Gender is required to create any kind of account on digital media, define the sizes of pant pockets, and decide how many paid leaves an employee needs after having a child. Gender not only serves as evidence of the patriarchal systems and policies that exist around us, but their discussion on the classification system and its establishment through time highlights its role in creating hierarchies within gender in relation to race and disability.
The authors cover a range of case studies, from social media platform’s data collection practices that reinforce the gender binary to government agencies’ classifications based on names to the size of the pockets on the pants of women as compared to men. D’Ignazio and Klein establish the need for quantitative and qualitative research methods. Interviews conducted around the maternal mortality rates discussed in Chapter 1 show how the families of women who had died were unaware of the fact that the issue extended beyond just them. “Without quantitative research … it is difficult to distinguish between personal experience and collective oppression” (98). However without qualitative data, collective oppression cannot be identified. This argument can be seen as informed by a long-standing feminist inquiry of ‘the personal is political.’ They discuss the evolution in Facebook’s gender classification system, which moved from the binary to an expanded range of choices to a blank box allowing users to input their gender. Yet, the back-end continued to classify users based on the gender binary for the convenience of their advertisers. The authors strategically use this example to describe the need for classification while critiquing the ideology of “that’s just the way things are.” Their employment of case studies that are day-to-day or close to home—such as an 8-year-old sharing the same name as someone placed on a terrorist watch list—grounded in academic theories allows the reader to themselves come to the point of challenging the binaries and the counting and classification systems. These case studies allow the reader to easily understand the concerns around classification that they pose and identify some of these biases within their daily lives as well.
While they provide a strong critique of classification practices, they also concede to the benefits of data collection and classification and ground their argument in questioning the origin of the classification methods. This is an important feminist and decolonial practice, of questioning the norms or the status quo and interrogating the hierarchies they enable, that the authors are engaging with here. Their concession to the benefits of counting is not an empty gesture since they provide methods around how counting can be done in a non-harmful manner. They propose that “questions about counting must be accompanied by questions about consent, as well as of personal safety, cultural dignity, and historical context” (115, emphasis mine). They discuss the methodology behind the corpus curated for the Colored Conventions Project and the tensions it navigates gracefully around engaging with information from, about, and for marginalized human beings. The care, consent, and cultural sensibility used in this archive demonstrates that “when deliberately considered, and when consent is obtained, counting can contribute to efforts to increase valuable and desired visibility” (118). Thus, they remind the reader that the “data” being talked about and collected is linked to real, living human beings. So, while visibility and being counted is important, we must be aware of the precarity this visibility causes to certain groups of people. The risks around making people visible can range from exposure to violence. A simple way of being ethical in data collection is asking for active consent.
This chapter serves as a reminder that “data” in the field of Humanities (and to a large extent outside the field) looks different than the traditional understanding of data. Data in the humanities can be human subjects, artifacts owned/created by humans, or information about humans. There is no objectivity in data collection; there is someone who gets counted, someone who does the counting, and someone who does not get counted because of the discriminatory, sexist, racist methods of counting and data collection. The authors’ generous use of real-life examples provides diverse contexts and demonstrates the corporeal consequences of “what gets counted and what doesn’t.” Their use of visuals in this chapter is key and works in synergy with the theory they provide. The charts and graphs are not just token visuals to cover another modality; instead, they provide important information and context, which allows the reader, especially the visual learner, to get a better grasp on their argument. While D’Ignazio and Klein do not provide specific methods and practices to counter the binaries and hierarchies in this chapter, I believe that this absence is justified because the conversation around the critique and consequences of binaries and hierarchies demands a separate space to bring the necessary awareness when we are engaging in data research. I also enjoyed seeing how they are “practicing what they preach” in the sense that they argue for more mindfulness and inclusivity in counting, showing us how it can be done through the diverse case studies and projects that they reference.
Chapter Review of ‘Data Feminism’ by Catherine D’Ignazio and Lauren Klein. Chapter 4.
