Offending Patterns in Cases of Sexual Violence Committed by Multiple Perpetrators
Bibliographic record
Abstract
Sexual violence committed by multiple perpetrators is a particularly worrying phenomenon given that the severity of psychopathological sequelae for the victims are increased when the sexual offense is committed by more than one offender. Preliminary studies showed that sexual violence committed by solo offenders is different from that of duos or groups of offenders. This study explores the heterogeneity within 983 cases of sexual violence committed by multiple perpetrators, with a focus not only on offenders’ modus operandi, but also victims’ routine activities and situational aspects of the crime. Results from a latent class analysis identified four offending patterns: sexual violence committed by multiple perpetrators where: 1) stranger victims were randomly selected; 2) offenders were geographically mobile; 3) victims were assaulted during social events; and 4) offenders were non-sexually motivated. Findings help identify situational characteristics interacting with offenders’ behaviors and victims’ routine activities associated with sexual violence committed by multiple perpetrators. The implications regarding the heterogeneity of criminal patterns in these forms of violence is discussed in relation to police practice, situational crime prevention strategies, and future research.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".