Men’s Sexual Violence Against Women: A Systematic Review of Self-Reported Measures of Perpetration
Bibliographic record
Abstract
Research has studied men's perpetration of sexual violence against women using various self-reported measures. A major difference among these measures is the types of perpetration tactics they assess. Measures having a broader range of tactics tend to detect higher perpetration rates. Yet, it is unclear whether these measures perform better on other aspects of reliability and validity as well. This review aimed to identify the available measures of men's sexual violence perpetration against women and to review the types of tactics and psychometric evidence of these measures. A systematic search was conducted in nine databases to identify articles that used a standardized self-report measure to assess men's sexual violence perpetration against women. The final sample included 85 articles, and 13 unique measures were identified from these articles. Results showed that three broad types of tactics were included in these measures: use of physical force, use of substances, and verbal coercion. Some measures also included a type of physical tactic that was manipulative or coercive but not necessarily forceful. Only one measure captured all types of tactics identified. Psychometric evidence was available for 12 measures, but the evidence regarding which measure provides the most accurate perpetration rates was inconclusive due to a lack of replications. This review highlighted the need for improving measures of sexual violence perpetration. Measures of perpetration should include a comprehensive range of tactics to increase validity, and more research is needed to examine test-retest reliability, false positives, and false negatives in responses to perpetration 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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".