Data and Farming: Uncovering Tensions in Food Justice

• In Event: Critical Narratives From Carceral, Educational, and Community Contexts

Abstract

Our study investigates tensions inherent in employing data science for social justice. Grounded in situated and consequential learning, our study employs a case-study methodology and analysis techniques from interaction and conversation analysis. Collaborating with three undergraduate students and an urban farm, the students used data science practices to highlight inequities surrounding food justice and access to food. Our findings reveal two key tensions: (1) the undergraduates' discourse on simplicity versus complexity in utilizing data science for social justice; and (2) the successful application of data science by the students in their food justice project, culminating in a presentation to the farm's director. We conclude by discussing implications for research and the use of data science in social justice projects.

Authors

• Marc Sager, Southern Methodist University

• Maximilian Sherard, University of North Texas

• Anthony J. Petrosino, Southern Methodist University