Women and Allies in Action: College Students as 'Diversity Workers' in the Activism Classroom
Bibliographic record
Abstract
Research on feminist pedagogy has analyzed activism-based teaching practices in introductory courses and special topics courses in Women’s and Gender Studies (WGS). Few studies have focused on courses that entirely center on feminist activism and have students implement weeks-long activism projects. In this article, I investigate how we can transfer an activist consciousness to our students, some of whom might not consider themselves feminists, might not have thought of themselves as activists, have not participated in any form of activism, or might be taking a WGS course only for general education or diversity credit. Using data collected in my “Women and Allies in Action” class via surveys, interviews, and analysis of students’ reflective writing, I assess which challenges hold students back and what motivates them to create and implement complex, creative, and sustainable feminist activism projects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.747 | 0.215 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.477 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.489 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".