Gender Differences in Sexual Violence Perpetration Behaviors and Validity of Perpetration Reports: A Mixed-Method Study
Bibliographic record
Abstract
The current mixed-method study examined gender differences in sexual violence (SV) perpetration behaviors and the validity of perpetration reports made on the Sexual Experiences Survey-Short Form Perpetration (SES-SFP). Fifty-four university students (31 women and 23 men) were asked to think out loud while privately completing an online version of the SES-SFP and to describe (typed response) behaviors that they reported having engaged in on the SES. Those who reported no such behavior were asked to describe any similar behaviors they may have engaged in. Integration of the quantitative responses on the SES and the qualitative descriptions of the events reported showed that men's SV perpetration was more frequent and severe than women's. The qualitative event descriptions further suggested that men's verbal coercion was often harsher in tone and that men more often than women used physical force (including in events only reported as verbal coercion on the SES). Unlike men, women often reported that their response to a refusal was not intended to pressure their partner or obtain the sexual activity. Two women also mistakenly reported experiences of their own victimization or compliance (giving in to unwanted sex) on SES perpetration items, which inflated women's SV perpetration rate. Findings suggest that quantitative measurement can miss important qualitative differences in women and men's behaviors and may underestimate men's and overestimate women's SV perpetration. Participants also sometimes misinterpreted or described confusion around the SES items, suggesting a need for updated language on this and other quantitative measures.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".