Pro-feminist men’s insights on educating men about rape culture
Bibliographic record
Abstract
Particularly in the context of the #metoo movement that confronts boys’ and men’s sexual violence against girls and women, the mere mention of rape culture can give rise to emotional responses that acknowledge it as a characteristic of patriarchy or staunchly deny its existence altogether. Yet, rape culture is not just an idea. It is a concept that captures the daily, real-life, on-the-ground sexual harassment, exploitation, and assault that so many girls and women experience all over the world. Rape culture is far broader than rape. The “culture” aspect includes implicit sexism evident in patterns of speech, objectification of girls and women disseminated through multiple media venues, and norms and attitudes that privilege boys and men over girls and women. The presumption that boys and men are sexually entitled to girls and women, so evident in gender socialization, is pervasive and foundational to perpetuating rape culture. We interviewed pro-feminist men about how they understand the idea of “rape culture” and how their ideas inform them about what men can do to mitigate rape cultural ideas and behaviours among boys and men. Implications for formal and informal education are discussed.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.026 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".