Sexual Coercion within Mixed-Sex Couples: The Roles of Sexual Motives, Revictimization, and Reperpetration
Bibliographic record
Abstract
Research suggests that a history of childhood sexual abuse, and previous experiences of sexual coercion, may predict sexual coercion victimization and perpetration. More recently, sexual motivation has been found to correlate with both consensual and non-consensual sexual activity. However, sexual motivation has not been examined in association with previous experiences of abuse and sexual coercion. The aim of this study was to investigate childhood sexual abuse, previous sexual coercion experiences, and sexual motives of both partners as possible risk factors for current sexual coercion victimization and perpetration within a sample of 209 mixed-sex couples. This study examined whether power, stress relief, partner pressure, and imposition motives contributed unique variance to the prediction of sexual coercion beyond that accounted for by past childhood sexual abuse and sexual coercion events. Using hierarchical logistic regressions, four predictive models were examined for both male and female sexual coercion perpetration and victimization. Results show that childhood sexual abuse was only a significant predictor of female sexual coercion perpetration, whereas male sexual coercion victimization and perpetration were predicted by sexual coercion victimization and perpetration in previous relationships. Power motives were also significant predictors of sexual coercion perpetration, and imposition was a significant predictor of sexual coercion victimization for both genders.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".