Weaponizing white feminism on campus in responses to sexualized violence
Bibliographic record
Abstract
In response to public and legislative pressure, Canadian post-secondary institutions (PSIs) have recently established new policies, protocols, and programming to respond to the issue of sexualized violence. This article offers an analysis of how White Feminist expertise is given a place at the tables of PSIs when creating sexualized violence response policies and practices. Based on qualitative interviews with over 40 feminist faculty across what is (colonially known) as Canada, we argue that White Feminist approaches, which are more palatable to university administrators, are prioritized while more transformative approaches to sexualized violence are excluded. By taking account of feminist faculty concerns with how some feminists have aligned themselves with institutional responses, our research calls attention to the harms of sexualized violence responses that dismiss intersectional, anti-carceral, and decolonial analyses of power.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.038 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".