An imperfect feminist journey: Reflections on the process to develop an effective sexual assault resistance programme for university women
Bibliographic record
Abstract
The work of feminist researchers to develop sexual assault resistance education programmes for women builds on the early work by feminist activists, self-defence instructors and other educators who stressed the importance of self-defence training for women. My research programme is strongly allied with this feminist herstory. My goals when I began were: to put feminist and social psychological theories into practice; to expand and reinforce young women’s knowledge and skills so that they are better able to defend themselves against sexual coercion and assault by known men; and to facilitate broader social change on sexual assault, at least on my own campus and city. There have been four major areas where anticipated and unanticipated conflicts or dilemmas between my feminist values and beliefs and my practice arose. These were: (1) keeping responsibility on male perpetrators while designing and offering programmes for women; (2) making male responsibility and female empowerment palatable to young women; (3) facing the limitations of an individual approach to a social problem; and (4) making the research conform to granting agency expectations. This article is my attempt to make visible the feminist struggles and successes that I encountered on the journey.
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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 | 0.036 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.022 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".