Decolonizing Or Doing the Best With What We Have? Feminist University-Community Engagement Outside WGSS Programs
Bibliographic record
Abstract
Feminist scholars and activists have a long history of integrating feminist praxis in the curriculum through community engagement initiatives. Using feminist critiques, they have investigated possibilities as well as limitations of these initiatives in neoliberal universities (Boyd & Sandell, 2012; Costa & Leong, 2012; Dean et al., 2019; Johnson & Luhmann, 2016; Kwon & Nguyen, 2016). Nevertheless, most of the existing studies focus on feminist community engagement within institutionalized Women’s, Gender, and Sexuality Studies (WGSS) departments, programs, and courses. This article demonstrates how feminist community engagement can expand its scope outside the institutional boundaries of WGSS programs. It contributes to the existing feminist literature in several ways. First, it explores how feminist and decolonial praxis can manifest in a non-WGSS setting and the resulting challenges and possibilities that arise. Second, it argues that the transition from traditional service learning to feminist and decolonial community engagement is a complex, contentious, and iterative process rather than an end goal. Lastly, it elaborates on how faculty can not only avoid the tendency of “learning elsewhere” and framing the community as “unprivileged Other” but also build and organize with community through creative subversion of various structures of the neoliberal university.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".