STS 363
Data for Justice Research Practicum Spring 2025
Division II Quantitative/Formal Reasoning Difference, Power, and Equity
Cross-listed WGSS 363 / INTR 350 / AMST 363

Class Details

Civil rights activist, educator, and investigative journalist Ida B. Wells said that “the way to right wrongs is to shine the light of truth upon them.” In this inclusive, collaborative, research-based course, students will bring statistical, computational, and/or mathematical approaches to bear on issues of social justice. Guided closely by the instructor, students will work in groups to carry out original research in an area such as criminal justice, education equity, environmental justice, health care equity, economic justice, or inclusion in arts/media. Prior research experience is not required; one goal of this course is to build skills for advanced research.
The Class: Format: seminar; This course is an intensive research practicum. Formation of research groups and selection of research topics will be facilitated by the instructor. The primary modality of work is peer collaboration.
Limit: 10
Expected: 10
Class#: 3014
Grading: no pass/fail option, no fifth course option
Requirements/Evaluation: To move towards a non-hierarchical, transparent, and egalitarian grading system, the instructor adopts a mastery-based, ungraded assessment framework.
Prerequisites: INTR 150 (Data for Justice), or prior equivalent exposure to computing, statistics, and social justice topics as approved by the instructor.
Enrollment Preferences: Students who have a declared major in Division I or II, who meet the prerequisites of the course, and who fill out the instructor's preregistration survey (contact the instructor for link).
Distributions: Division II Quantitative/Formal Reasoning Difference, Power, and Equity
Notes: This course is cross-listed and the prefixes carry the following divisional credit:
WGSS 363 Division II STS 363 Division II INTR 350 Division II AMST 363 Division II
DPE Notes: Students will research issues of social justice in areas such as criminal justice, arts/media, environmental justice, education, and health care, and along identity axes such as gender, race/ethnicity, disability status, and sexual orientation.
QFR Notes: Students will use multiple mathematical, statistical, and computational frameworks to acquire, model, and analyze real-world data.

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