AMST 363
Mathematical and Computational Approaches to Social Justice Fall 2022 (also offered Spring 2023)
Division II Quantative/Formal Reasoning Difference, Power, and Equity
Cross-listed STS 363 / WGSS 363 / AMST 363 / MATH 308

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 research-based tutorial, students will bring the vanguard of quantitative approaches to bear on issues of social justice. Each tutorial group will carry out a substantial project in an area such as criminal justice, education equity, environmental justice, health care equity, economic justice, or inclusion in arts/media. All students should expect to invest substantial effort in reading social justice literature and in acquiring new skills in data science.
The Class: Format: tutorial; This is a research-based tutorial.
Limit: 20
Expected: 20
Class#: 1965
Grading: yes pass/fail option, no fifth course option
Requirements/Evaluation: To move towards a non-hierarchical, transparent, and egalitarian grading system, the instructor follows an "ungrading" methodology.
Prerequisites: Across each 3 - 5 person tutorial group: multivariable calculus (e.g., Math 150/151), linear algebra (e.g., Math 250), statistics (e.g., Stat 161/201), computer programming (e.g., Comp 134), some working knowledge of or interest in social justice issues.
Enrollment Preferences: Students will be admitted in groups of 3 - 5 based on a proposal submitted prior to registration. The instructor is happy to facilitate formation of groups and to give feedback on draft proposals. Contact the instructor early, prior to preregistration.
Distributions: Division II Quantative/Formal Reasoning Difference, Power, and Equity
Notes: This course is cross-listed and the prefixes carry the following divisional credit:
STS 363 Division II WGSS 363 Division II AMST 363 Division II MATH 308 Division III
DPE Notes: Students study issues of equity, diversity, and inclusion 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 use multiple mathematical, statistical, and computational frameworks to acquire, model, and analyze real-world data.

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