Feminist Pedagogy for Teaching Online

Subject: Critical Data Justice Literature

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.

Creative Data Literacy: A Constructionist Approach to Teaching Information Visualization

Author(s):	D’ignazio, C. Bhargava, R.
Date:	2018
Publication:	Digital Humanities Quarterly
Citation:	D’ignazio, C. & Bhargava, R. (2018). Creative Data Literacy: A Constructionist Approach to Teaching Information Visualization. Digital Humanities Quarterly, 12(4). https://hdl.handle.net/1721.1/123473

Section on webpage:	Critical Data Justice Literature
Tenets:	Using technology intentionally to build communities and enhance learning.
Annotation:	(Abstract) Data visualization has rapidly become a standard approach to interrogating and understanding the world around us in domains that extend beyond the technical and scientific to arts, communications and services. In business settings the Data Scientist has become a recognized and valued role [Davenport and Patil 2012]. Journalism has re-oriented itself around data-driven storytelling as a potential saviour for an industry in peril [Howard 2014]. Governments are moving to more data-driven decision making, publishing open data portals and pondering visualization as an opportunity for citizen participation [Gurstein 2011]. This journal itself has numerous examples that use visualization tools and techniques within the digital humanities as a tool for exploration [Roberts-Smith et al. 2013] [Hoyt, Ponto, and Roy 2014] [Forlini, Hinrichs, and Moynihan 2016]. This boom in attention has led large new populations of learners into the field. Formal educational settings have rushed to create new approaches and introductions to this content, but often they fall back on traditional approaches to things such as scientific charting and graphing [Webber et al. 2014] [Calzada and Marzal 2013]. Many view data visualization as a new technology, which runs the risks of replicating old approaches without acknowledging the unique affordances and domains that data visualization relies upon. Data visualization is not simply another technology to integrate into education. It is visual argument and persuasion, far more closely associated with rhetoric and writing than spreadsheets [Zer-Aviv 2014]. In this paper we present novel approaches to learning technologies and activities, focused on novice learners entering the field of data driven storytelling. We begin with a deeper dive into the problems we see with introducing new learners into a field characterized by inequality, continue with a discussion of approaches for introducing technologies to education, and summarize the inspirational pedagogies we build on. We then offer some design principles and three activities as examples of the concept of creative data literacy. We assert that creative approaches grounded in constructionist educational theories are necessary to empower non-technical learners to be able to tell stories and argue for change with data.

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.