“She didn’t want to…and I’d obviously insist”: Canadian University Men’s Normalization of their Sexual Violence Against Intimate Partners
Bibliographic record
Abstract
Men’s sexual violence against women is pervasive and is especially common in heterosexual intimate relationships. Little research has examined sexual violence in this relational context and from men’s perspectives, including how they talk about and frame their behavior. The current research examined how men’s sexual violence and accounts thereof reflected and enacted the normalization of violent heterosexuality. We used online surveys with 447 Canadian university men to screen for men who had used sexual violence in their most recent past or present relationship with a woman. Of these men, 71 (15.88%) reported at least one experience using sexual violence and 10 of these men participated in an in-depth interview to elaborate on their experiences. We used a feminist poststructuralist form of discourse analysis to analyze the interview transcripts. Results suggested that men often used language that helped them to position themselves and their sexual violence as normal and expected. However, they also often used alternative discourses and accounts about sexual violence, heterosexuality, and consent. We briefly discuss the implications of our results for educational campaigns and interventions.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".