Yes, (most) men know what rape is: A mixed-methods investigation into college men’s definitions of rape.
Bibliographic record
Abstract
Sexual violence, including rape, is a pervasive problem on college campuses in the United States. Although men perpetrate the majority of sexual violence, men's attitudes, experiences, and perspectives are not typically included in research on rape and sexual violence. We addressed this empirical gap through our mixed-methods analysis of 365 college-aged men's definitions of the term "rape." Our analysis via consensual qualitative research revealed that men's definitions fit into nine primary domains: lack of consent, taken advantage of, sex, sexual activity, unwanted, gender/sex-specific, harm to victim, relationship, and emotional response, as well as a miscellaneous domain. Further, using chi-square tests of independence, we compared responses from men with and without histories of sexual violence perpetration. Findings showed that the definitions generated by men with a history of perpetration were less likely to include nonpenetrative sexual violence and were more likely to use gender/sex-specific language. We conclude that most young men have a generally accurate understanding of rape, though perpetrators' understandings may be somewhat narrower and more limited than those without a history of perpetration. We end with recommendations for refocusing sexual education curricula to better aid in the prevention of sexual violence perpetration. Specifically, given that (most) men know what rape is, educators should emphasize the cultural and situational factors that make rape more likely so all people can reduce the risk of sexual violence and take proactive precautions to prevent it.
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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 | 0.043 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".