Reclaiming data science for just geographies: A critical approach.
As data science increasingly shapes educational programmes, research agendas, and societal narratives, its practices have come under scrutiny for reinforcing historical inequalities, perpetuating biases, and neglecting critical engagement with issues of power, capital, and representation. Drawing up...
| Publicado en: | Big Data & Society Vol. 13; no. 2; pp. 1 - 18 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Sage Publications Inc.
Apr-Jun2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=194971664&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 194971664 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 20539517 KG5N jtl: Big Data & Society issn: 20539517 maglogo: Y pubinfo: dt: Apr-Jun2026 vid: 13 iid: 2 pid: 344 pub: Sage Publications Inc. artinfo: ui: 194971664 10.1177/20539517261426463 ppf: 1 ppct: 17 formats: tig: atl: Reclaiming data science for just geographies: A critical approach. aug: au: van Geene, Laura Goncalves, Juliana Robinson, Caitlin Verma, Trivik affil: Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands Faculty of Architecture and the Built Environment, Delft University of Technology, Delft, The Netherlands School of Geographical Sciences, University of Bristol, Bristol, UK Geography and Environment, Loughborough University, Loughborough, UK su: Data science Decolonization Human geography Social stratification Intersectionality Social justice Power (Social sciences) Science education sug: subj: Data science Decolonization Human geography Social stratification Intersectionality Social justice Power (Social sciences) Science education keyword: Critical data science decoloniality education intersectionality radical transdisciplinarity reflexivity ab: As data science increasingly shapes educational programmes, research agendas, and societal narratives, its practices have come under scrutiny for reinforcing historical inequalities, perpetuating biases, and neglecting critical engagement with issues of power, capital, and representation. Drawing upon critical social science theories including decoloniality, intersectionality, radical transdisciplinarity, and reflexivity, this paper narratively explores the limitations of conventional data science methods and pedagogy, advocating instead for a critical paradigm shift aimed at reclaiming data science for just geographies. We highlight the necessity for an approach that recognises data science as inherently subjective, deeply embedded in social and political contexts, and fundamentally shaped by historical legacies of colonialism and exclusion. By situating our experiences within universities in Western Europe, we illustrate how education and research can inadvertently perpetuate harmful structures when failing to critically engage with the positionalities and power dynamics inherent to data practices. Responding to these broader societal challenges, we propose a practical, iterative framework for critical data science that has emerged from our teaching methods and research experiences. This framework invites researchers and educators to continually reflect upon inclusivity, inequality, participation, power, and positionality throughout each stage of the data science process. Ultimately, our aim is to empower a generation of data scientists capable of interrogating dominant narratives, embracing diverse perspectives, and collaboratively working towards more equitable, just, and caring futures for all. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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