The Numbers Don’t Speak for Themselves

Author(s): D’Ignazio, C. Klein, L. F.
Date: 2020
Publication: Data Feminism
Citation: D’Ignazio, C. & Klein, L. F. (2020). The Numbers Don’t Speak for Themselves. In Data Feminism (149-172). MIT Press. https://doi.org/10.7551/mitpress/11805.003.0008
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: In this chapter of Data Feminism, D’Ignazio and Klein introduce the principle of considering context, and walk through situating data on the web, viewing data as partisan, communicating context, and restoring context. Data feminism asserts that data are not neutral or objective. They are the products of unequal social relations, and this context is essential for conducting accurate, ethical analysis. The authors begin the chapter with an error made by media sources referencing the Global Database of Events, Language and Tone (GDELT), a database that, like many others, is characterized by a totalizing and dominating framework as enacted through data capture and analysis. They state that the contextualization of data is just as important as its availability, and provide the United States’s and Brazil’s apparent data transparency as examples; although the data is in theory available to the public, a lack of metadata and understanding of the government systems from which the data originate make it practically inaccessible to possible users. In this light, the authors advocate for a viewing of all data as “cooked” – that is, already a product of numerous social relations and data sorting methods.

 

Data visualization literacy: A feminist starting point

Author(s): D’Ignazio, C. & Bhargava, R.
Date: 2020
Publication: Data Visualization in Society
Citation: D’Ignazio, C., & Bhargava, R. (2020). Data visualization literacy: A feminist starting point. In M. Engebretsen & H. Kennedy (Eds.), Data Visualization in Society (pp. 207–222). Amsterdam University Press. https://doi.org/10.2307/j.ctvzgb8c7.19
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) We assert that visual-numeric literacy, indeed all data literacy, must take as its starting point that the human relations and impacts currently produced and reproduced through data are unequal. Likewise, white men remain overrepresented in data-related fields, even as other STEM (Science, Technology, Engineering and Medicine) fields have managed to narrow their gender gap. To address these inequalities, we introduce teaching methods that are grounded in feminist theory, process, and design. Through three case studies, we examine what feminism may have to offer visualization literacy, with the goals of cultivating self-efficacy for women and underrepresented groups to work with data, and creating learning spaces were, as Philip et al. (2016) state, ‘groups influence, resist, and transform everyday and formal processes of power that impact their lives.’

 

The visible body and the invisible organization: Information asymmetry and college athletics data

Author(s): Greene, D. Beard, N. Clegg, T. & Weight, E.
Date: 2023
Publication: Big Data & Society
Citation: Greene, D., Beard, N., Clegg, T., & Weight, E. (2023). The visible body and the invisible organization: Information asymmetry and college athletics data. Big Data & Society, 10(1). https://doi.org/10.1177/20539517231179197
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) Elite athletes are constantly tracked, measured, scored, and sorted to improve their performance. Privacy is sacrificed in the name of improvement. Athletes frequently do not know why particular personal data are collected or to what end. Our interview study of 23 elite US college athletes and 26 staff members reveals that their sports play is governed through information asymmetries. These asymmetries look different for different sports with different levels of investment, different racial and gender makeups, and different performance metrics. As large, data-intensive organizations with highly differentiated subgroups, university athletics are an excellent site for theory building in critical data studies, especially given the most consequential data collected from us, with the greatest effect on our lives, is frequently a product of collective engagement with specific organizational contexts like workplaces and schools. Empirical analysis reveals two key tensions in this data regime: Athletes in high-status sports, more likely to be Black men, have relatively less freedom to see or dispute their personal data, while athletes in general are more comfortable sharing personal data with people further away from them. We build from these findings to develop a theory of collective informational harm in bounded institutional settings such as the workplace. The quantified organization, as we term it, is concerned not with monitoring individuals but building data collectives through processes of category creation and managerial data relations of coercion and consent.

 

Critical data studies: An introduction

Author(s): Iliadis, A. & Russo, F.
Date: 2016
Publication: Big Data & Society
Citation: Iliadis, A., & Russo, F. (2016). Critical data studies: An introduction. Big Data & Society, 3(2). https://doi.org/10.1177/2053951716674238
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) Critical Data Studies (CDS) explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles.

 

Exploring Approaches to Data Literacy Through a Critical Race Theory Perspective

Author(s): Johnson, B. Shapiro, B. R. DiSalvo, B. Rothschild, A. & DiSalvo, C.
Date: 2021
Publication: Learning Sciences Faculty Publications
Citation: Johnson, B., Shapiro, B.R., DiSalvo, B., Rothschild, A., & DiSalvo, C. 2021. Exploring Approaches to Data Literacy Through a Critical Race Theory Perspective. Learning Sciences Faculty Publications, 40. https://doi.org/10.1145/3411764.3445141
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) In this paper, we describe and analyze a workshop developed for a work training program called DataWorks. In this workshop, data workers chose a topic of their interest, sourced and processed data on that topic, and used that data to create presentations. Drawing from discourses of data literacy; epistemic agency and lived experience; and critical race theory, we analyze the workshops’ activities and outcomes. Through this analysis, three themes emerge: the tensions between epistemic agency and the context of work, encountering the ordinariness of racism through data work, and understanding the personal as communal and intersectional. Finally, critical race theory also prompts us to consider the very notions of data literacy that undergird our workshop activities. From this analysis, we offer a series of suggestions for approaching designing data literacy activities, taking into account critical race theory.

 

Intersectional approaches to data: The importance of an articulation mindset for intersectional data science

Author(s): Bentley, C. Muyoya, C. Vannini, S. Oman, S. & Jimenez, A.
Date: 2023
Publication: Big Data & Society
Citation: Bentley, C., Muyoya, C., Vannini, S., Oman, S., & Jimenez, A. (2023). Intersectional approaches to data: The importance of an articulation mindset for intersectional data science. Big Data & Society, 10(2). https://doi.org/10.1177/20539517231203667
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) Data’s increasing role in society and high profile reproduction of inequalities is in tension with traditional methods of using social data for social justice. Alongside this, ‘intersectionality’ has increased in prominence as a critical social theory and praxis to address inequalities. Yet, there is not a comprehensive review of how intersectionality is operationalized in research data practice. In this study, we examined how intersectionality researchers across a range of disciplines conduct intersectional analysis as a means of unpacking how intersectional praxis may advance an intersectional data science agenda. To explore how intersectionality researchers collect and analyze data, we conducted a critical discourse analysis approach in a review of 172 articles that stated using an intersectional approach in some way. We contemplated whether and how Collins’ three frames of relationality were evident in their approach. We found an over-reliance on the additive thinking frame in quantitative research, which poses limits on the potential for this research to address structural inequality. We suggest ways in which intersectional data science could adopt an articulation mindset to improve on this tendency.