Behavioral and psychological predictors of multiple-perpetrator rape proclivity: A community sample study of men
Bibliographic record
Abstract
Most of the sexual violence research focuses on incidents involving a single offender, yet one-fourth to one-third of rapes involved multiple offenders (Horvath & Kelly, 2009). The present study aimed to build upon the multiple-perpetrator rape (MPR) literature by investigating potential correlates associated with a proclivity for MPR and reconfirming prior findings. Community men completed a series of questionnaires that included the Multiple-Perpetrator Rape Interest Scale, the UCLA Loneliness Scale: Short-Form, the Buss–Perry Aggression Questionnaire: Short Form, the Sexual Fantasy Questionnaire, the Anger Rumination Scale, the Measure for Assessing Subtle Rape Myths, and the Self-Report Psychopathy-III: Short Form. The strongest relationship for M-PRIS was SFQ Sado-Masochistic (r = .79, p < .001). In a multiple linear regression, results showed a significant model, F(6, 108) = 28.6, p < .05, which explained 61.4% of the variance in a proclivity for MPR. Specifically, BPAQ Total, SFQ Total, and ARS Total were significant (sr 2 = .0543, 0.0499, 0.0488, respectively). One implication is for clinicians to target various types of aggression, deviant sexual fantasies, and anger rumination in therapy among those with an interest to commit MPR to potentially reduce the urge to commit the action. Educational and preventive initiatives aimed at addressing sexual violence behaviors may also gain insights into individuals prone to engaging in such behaviors, where these programs seek to diminish the likelihood of MPR occurrences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".