OFFENDER–VICTIM INTERACTION AND CRIME EVENT OUTCOMES: MODUS OPERANDI AND VICTIM EFFECTS ON THE RISK OF INTRUSIVE SEXUAL OFFENSES AGAINST CHILDREN*
Bibliographic record
Abstract
Criminological research has shown the relevance of examining offender–victim interaction and related factors to understand crime event outcomes. In sexual offenses against children, an obvious lack of knowledge exists regarding this issue. From a criminological perspective, we seek to improve our understanding of the offender–victim interaction in sexual offenses against children and, in particular, what factors might increase the risk of a more intrusive offense. We argue that modus operandi strategies play a central role in crime event outcomes and examine this hypothesis with data obtained from a semistructured interview conducted with offenders. As expected, modus operandi was found to have a strong effect on crime event outcomes, especially victim participation during sexual episodes. Victim effects also emerged from the analyses. Specifically, a strong interaction effect between age and gender of the victim was found for victim participation, which suggests that as the victim gets older, offenders are more likely to make their victim participate in sexual episodes when abusing a male victim but are less likely to do so when abusing a female victim.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".