Sexual Violence Perpetration and Victimization: Providing Prevalence Rates for Understudied Populations
Bibliographic record
Abstract
Sexual violence research has been subjected to gender and heteronormative biases. It has been customary to focus on men as perpetrators and women as victims and to exclude sexual and gender minorities from protocols, which has led some demographic groups to be underrepresented. This article aimed to (1) provide prevalence rates for sexual violence perpetration and victimization in understudied populations, and (2) compare rates recorded by these understudied populations to a heterosexual men reference group for perpetration and a heterosexual women reference group for sexual victimization. A sample of 1796 individuals (age 16–83) representing diverse gender identities and sexual orientations completed modified, gender-inclusive versions of the Sexual Experiences Survey—Tactics first Perpetration and Victimization. Results indicate that (1) heterosexual men, transgender/nonbinary individuals, homosexual women, non-monosexual women, and homosexual men registered perpetration rates over 30%; (2) non-monosexual and heterosexual women recorded the highest rates of sexual victimization; (3) heterosexual men reported statistically higher rates of perpetration and lower rates of victimization than heterosexual women; (4) sexual and gender minorities reported perpetration rates that are statistically equivalent to heterosexual men and victimization rates that are statistically equivalent to heterosexual women; and (5) verbal coercion was the most commonly used strategy by all subgroups. Findings suggest the need for prevention programs to target perpetration by all genders and behaviors outside of the traditional rape script, for victims' resources to be welcoming of men and sexual and gender minorities, and for efforts to be made in research to limit gender and heteronormative biases.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".