Becoming Racially Literate About Data and Data-Literate About Race: Data Visualizations in the Classroom as a Site of Racial-Ideological Micro-Contestations.

Author(s): Philip, T. M. Olivares-Pasillas, M. C. & Rocha, J.
Date: 2016
Publication: Cognition and Instruction
Citation: Philip, T. M., Olivares-Pasillas, M. C., & Rocha, J. (2016). Becoming Racially Literate About Data and Data-Literate About Race: Data Visualizations in the Classroom as a Site of Racial-Ideological Micro-Contestations. Cognition and Instruction, 34(4), 361–388. https://doi.org/10.1080/07370008.2016.1210418
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) Data visualizations are now commonplace in the public media. The ability to interpret and create such visualizations, as a form of data literacy, is increasingly important for democratic participation. Yet, the cross-disciplinary knowledge and skills needed to produce and use data visualizations and to develop data literacy are not fluidly integrated into traditional K–12 subject areas. In this article, we nuance and complicate the push for data literacy in STEM reform efforts targeting youth of color. We explore a curricular reform project that integrated explicit attention to issues pertaining to the collection, analysis, interpretation, representation, visualization, and communication of data in an introductory computer science class. While the study of data in this unit emphasized viewing and approaching data in context, neither the teacher nor the students were supported in negotiating the racialized context of data that emerged in classroom discussions. To better understand these dynamics, we detail the construct of racial literacy and develop an interpretative framework of racial-ideological micro-contestations. Through an in-depth analysis of a classroom interaction using this framework, we explore how contestations about race can emerge when data visualizations from the public media are incorporated into STEM learning precisely because the contexts of data are often racialized. We argue that access to learning about data visualization, without a deep interrogation of race and power, can be counterproductive and that efforts to develop authentic data literacy require the concomitant development of racial literacy.

 

Is literacy what we need in an unequal society?

Author(s): Pinney, L.
Date: 2020
Publication: Data Visualization in Society
Citation: Pinney, L. (2020). Is literacy what we need in an unequal society? In H. Kennedy & M. Engebretsen (Eds.), Data Visualization in Society (pp. 223–238). Amsterdam University Press. https://doi.org/10.1515/9789048543137-018
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) Having the skills and awareness to make sense of data visualizations has become a contributing factor in determining who gets to participate in our data-driven society. Initiatives that seek to enable people to make sense of some aspect of our digital, dataf ied worlds are often described in terms of literacy. However, taking a closer look at different usages of literacy across academia, policy, and practice reveals dif ferent notions of power embedded in different populations’ implicit understanding of the term. Situated in the emerging f ield of critical data studies, the f ield that is concerned with understanding data’s role in reproducing and creating social inequalities, this is a conceptual chapter that asks how useful literacy is in this context.

 

Data-bodies and data activism: Presencing women in digital heritage research

Author(s): Thompson, T. L.
Date: 2020
Publication: Big Data & Society
Citation: Thompson, T. L. (2020). Data-bodies and data activism: Presencing women in digital heritage research. Big Data & Society, 7(2). https://doi.org/10.1177/2053951720965613
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) As heritage-as-the-already-occurred folds into heritage-in-the-making practices, temporal and spatial fluidity is made more complex by digital mediation and particularly by Big Data. Such liveliness evokes ontological, epistemological and methodological challenges. Drawing on more-than-human theorizing, this article reframes the notion of data-bodies to advance data activist-oriented research in heritage. Focused primarily on women, it examines how their distributed agency and voice with respect to data practices and the (re)makings of (digital) heritage could be amplified. I describe three methodological directions, influenced by feminist work in critical data studies, which could be employed by researchers: attuning to and becoming with data, making data physical and changing narratives. From data-bodies to haunted data, performative data curation and mapping data-bodies, and attuning to data streams and re-voicing narratives, this article contributes to discussions of how to engage critically and creatively with the datafication of digital heritage practices, knowings and ontologies.

 

Contributions of Paulo Freire for a Critical Data Literacy: a Popular Education Approach

Author(s): Tygel, A. F. & Kirsch, R.
Date: 2016
Publication: The Journal of Community Informatics
Citation: Tygel, A. F. & Kirsch, R. (2016). Contributions of Paulo Freire for a Critical Data Literacy: a Popular Education Approach. The Journal of Community Informatics, 12(3), 108–121. https://doi.org/10.15353/joci.v12i3.3279
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) Paulo Freire is the patron of education in Brazil. His main work – the Popular Education pedagogy – influences many educators all over the world who believe in education as a way of liberating poor oppressed people. One of the outcomes of Freire’s work is a literacy method, developed in the 1960’s. In this paper, we propose the adoption of elements of Freire’s Literacy Method for use in a pedagogical pathway towards data literacy. After tracing some parallels between literacy education and data literacy, we suggest some data literacy strategies inspired on Freire’s method. We also derive from it a definition for critical data literacy.

 

Calling for a feminist revolt to decolonise data and algorithms in the age of Datification

Author(s): Vargas-Solar, G.
Date: 2022
Publication: International Forum 2022
Citation: Vargas-Solar, G. (2022). Calling for a feminist revolt to decolonise data and algorithms in the age of Datification. International Forum 2022- Decolonial Perspectives on Gender, Sexuality and Patriarchy: art, activism and academia. https://doi.org/10.48550/arXiv.2210.08965
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) Feminist and women groups, indigenous communities and scholars in the global south/north refusing to adhere to hegemonic datafication programs have started to organise and fight back from the inside. The first essential step is to show and problematise technological progress exhibiting the poverty, violence, exclusion, and cultural erase promoted by this “progress”. The second step is to promote technology, algorithmic and artificial literacy. Education is critical to learn how to revert and revoke the datified digital twin already colonising all Earth’s societies silently and with impunity. It is not the colonisation of body-territories; it goes beyond and occupies humanity’s mind’s essence, i.e., imagination and imaginary. Against the colonisation of the imaginary, militant groups are imagining and designing alternative algorithms, datasets collection strategies and appropriation methods. The paper discusses their actions and alternative thinking.

 

How do data come to matter? Living and becoming with personal data

Author(s): Lupton, D.
Date: 2018
Publication: Big Data & Society
Citation: Lupton, D. (2018). How do data come to matter? Living and becoming with personal data. Big Data & Society, 5(2). https://doi.org/10.1177/2053951718786314
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) Humans have become increasingly datafied with the use of digital technologies that generate information with and about their bodies and everyday lives. The onto-epistemological dimensions of human–data assemblages and their relationship to bodies and selves have yet to be thoroughly theorised. In this essay, I draw on key perspectives espoused in feminist materialism, vital materialism and the anthropology of material culture to examine the ways in which these assemblages operate as part of knowing, perceiving and sensing human bodies. I draw particularly on scholarship that employs organic metaphors and concepts of vitality, growth, making, articulation, composition and decomposition. I show how these metaphors and concepts relate to and build on each other, and how they can be applied to think through humans’ encounters with their digital data. I argue that these theoretical perspectives work to highlight the material and embodied dimensions of human–data assemblages as they grow and are enacted, articulated and incorporated into everyday lives.

 

Data Fail: Teaching Data Literacy with African Diaspora Digital Humanities

Author(s): Mahoney, J. Risam, R. & Nassereddine, H.
Date: 2020
Publication: The Journal of Interactive Technology & Pedagogy
Citation: Mahoney, J., Risam, R., & Nassereddine, H. (2020). Data Fail: Teaching Data Literacy with African Diaspora Digital Humanities. The Journal of Interactive Technology & Pedagogy, 18. https://jitp.commons.gc.cuny.edu/data-fail-teaching-data-literacy-with-african-diaspora-digital-humanities/
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) This essay examines the authors’ experiences working collaboratively on Power Players of Pan-Africanism, a data curation and data visualization project undertaken as a directed study with undergraduate students at Salem State University. It argues that data-driven approaches to African diaspora digital humanities, while beset by challenges, promote both data literacy and an equity lens for evaluating data. Addressing the difficulties of undertaking African diaspora digital humanities scholarship, the authors discuss their research process, which focused on using archival and secondary sources to create a data set and designing data visualizations. They emphasize challenges of doing this work: from gaps and omissions in the archives of the Pan-Africanism social movement to the importance of situated data to the realization that the original premises of the project were flawed and required pivoting to ask new questions of the data. From the trials and tribulations—or data fails—they encountered, the authors assess the value of the project for promoting data literacy and equity in the cultural record in the context of high school curricula. As such, they propose that projects in African diaspora digital humanities that focus on data offer teachers the possibility of engaging reluctant students in data literacy while simultaneously encouraging students to develop an ethical lens for interpreting data beyond the classroom.

 

Taking Data Literacy to the Streets: Critical Pedagogy in the Public Sphere

Author(s): Markham, A. N.
Date: 2020
Publication: Qualitative Inquiry
Citation: Markham, A. N. (2020). Taking Data Literacy to the Streets: Critical Pedagogy in the Public Sphere. Qualitative Inquiry, 26(2), 227–237. https://doi.org/10.1177/1077800419859024
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) This article describes an ongoing series of public arts–based experiments that build critical curiosity and develop data literacy via self-reflexive public interventions. Examined through the lens of remix methodology the Museum of Random Memory exemplifies a form of collective–reflexive meta-analysis whereby interdisciplinary researchers generate immediate social change and build better questions for future public engagement. The experiments help people critically analyze their own social lives and well being in cultural environments of growing datafication and automated (artificial intelligence [AI]-driven) decision-making. Reflexivity, bricolage, and critical pedagogy are emphasized as approaches for responding to changing needs in the public sphere that also build more robust interdisciplinary academic teams.

 

Critical data ethics pedagogies: Three (non-rival) approaches

Author(s): Murillo, L. F. R. Wylie, C. & Bourne, P.
Date: 2023
Publication: Big Data & Society
Citation: Murillo, L. F. R., Wylie, C., & Bourne, P. (2023). Critical data ethics pedagogies: Three (non-rival) approaches. Big Data & Society, 10(2). https://doi.org/10.1177/20539517231203666
Section on webpage: Critical Data Justice Literature
Tenets: Using technology intentionally to build communities and enhance learning.
Annotation: (Abstract) In a moment of heightened ethical questioning concerning data-intensive analytics, “data ethics” has become a site of dispute over its very definition in teaching, research, and practice. In this paper, we contextualize this dispute based on the experience of teaching data ethics. We describe how the field of computer ethics has historically informed the training of computer experts and how, in recent years, the scholarship on science and technology studies has created opportunities for transforming the way we teach with the inclusion of critical scholarship on relational ethics and sociotechnical systems. The emergent literature on “critical data ethics” has created a space for interdisciplinary collaboration that integrates technical and social science research to examine digital systems in their design, implementation, and use through a hands-on approach. As a contribution to the recent efforts to reimagine and transform the field of data science, we conclude with a discussion of the approach we devised to bridge technology/society divides and engage students with questions of social justice, accountability, and openness in their data practices.

 

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.