Citizen science as a data-based practice: A consideration of data justice

• Published in: Patterns (New York, N.Y.) N.Y.), 2021-04, Vol.2 (4), p.100224-100224, Article 100224
• Main Authors: Christine, Debora Irene, Thinyane, Mamello
• Format: Article
• Language: English
• Subjects: citizen science; data assemblage; data justice; data practice; data science; equity; marginalization; participation
• ISSN: 2666-3899; 2666-3899
• DOI: 10.1016/j.patter.2021.100224

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. •A “data justice” analytical framework was used to study citizen science•Five dimensions of data justice were explored in citizen science cases•Some forms of injustice were found in citizen science cases under review Citizen science has been regarded for its contribution to scientific research, inclusive science engagement, and addressing of social justice issues. Within citizen science, social justice is pursued through different approaches, including facilitating public participation in research and utilizing citizen science data in social justice advocacy. Although citizen science is a data-based practice, the structural dimensions of the data processes that support and hamper the pursuit of social justice in citizen science remain understudied. This article applies a “data justice” framework to unpack the elements and practices that constitute the generation, circulation, and use of data and data-related outcomes in citizen science. We demonstrate the relevance and limitations of the framework with regard to the domain of citizen science. This work thus contributes to the growing research interest in critical data studies, i.e., the study around equity issues in data science. Despite the burgeoning field of citizen science as a data-based practice, the structural issues of the data processes in citizen science remain understudied. T