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

Abstract

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.