Citizen science as a data-based practice: A consideration of data justice.
citizen science
data assemblage
data justice
data practice
data science
equity
marginalization
participation
Journal
Patterns (New York, N.Y.)
ISSN: 2666-3899
Titre abrégé: Patterns (N Y)
Pays: United States
ID NLM: 101767765
Informations de publication
Date de publication:
09 Apr 2021
09 Apr 2021
Historique:
received:
06
01
2020
revised:
27
09
2020
accepted:
25
02
2021
entrez:
13
5
2021
pubmed:
14
5
2021
medline:
14
5
2021
Statut:
epublish
Résumé
Citizen science has been motivated by several perspectives, including increased efficiency in data collection and distributed analysis, democratizing knowledge production, making science more responsive to community needs, and improving the representation of marginalized populations in public data. Despite the potential of citizen science to achieve social justice agendas through a data-intensive and data-driven participatory scientific enquiry, scholarship in critical data studies offers several problematizations of data-based practices, highlighting risks of exclusion and inequality. To understand the extent to which citizen science supports and challenges forms of injustice, this study used a "data justice" analytical framework to critically explore the assemblages of citizen science. We examined four citizen science cases with different levels of citizen engagement, intended outcomes, and data systems. The analysis suggests instances of injustice occurring throughout the data processes of the citizen science cases across the dimensions of procedural, instrumental, rights-based, structural, and distributive data justice.
Identifiants
pubmed: 33982019
doi: 10.1016/j.patter.2021.100224
pii: S2666-3899(21)00038-6
pmc: PMC8085591
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100224Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2021 The Authors.
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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