Call for Papers: Quantitative Justice
The goal of this volume is to provide examples of contemporary results and research related to quantitative justice and the intersections of data science and social justice. The theme of the volume is work that uses quantitative and qualitative techniques to address contemporary social justice issues.
Quantitative Justice (QJ) is defined as “the application of techniques, tools and topics from various quantitative sciences (e.g., applied mathematics, data science, computer science, etc.) in subject domains that are derived from the social sciences (e.g., political science, law, economics, etc.) with the goal of promoting social justice.” Data Science for Social Justice (DS4SJ) is broadly defined as “data scientific work (broadly construed) that actively challenges systems of inequity and concretely supports the liberation of oppressed and marginalized communities.” We believe that QJ is the more expansive term so we have selected this as the title of the volume, although we will happily accept work firmly characterized as DS4SJ.
Submission Deadline: November 1, 2024
Guest Editors:
- Omayra Ortega (Sonoma State University, USA)
- Carrie Diaz-Eaton (Bates College, USA)
- Ron Buckmire (Occidental College, USA)
- Joseph Hibdon, Jr. (Northeastern Illinois University, USA)
- Tian-An Wong (University of Michigan-Dearborn, USA)
- Victor Piercey (Ferris State University, USA)