Pathways to sexual aggression according to modus operandi in male and female perpetrators: A systematic review
Bibliographic record
Abstract
Over the last 40 years, studies have been carried out to investigate the pathways to sexual aggression. However, the heterogeneity of these pathways across studies remains unspecified, due to the use of diverse terminology. Consequently, the aim of the current systematic review was to investigate these pathways in various offender categories. A search of 21 online databases identified 29,218 candidate studies, of which 23 specifically examined pathways to sexual aggression. These pathways include developmental, personality, lifestyle, disinhibitor, and modus operandi characteristics. In the 23 studies, which used a variety of quantitative and qualitative methodologies, we found 7 prototypical pathways. Some are specific to one category of sexual aggressor (e.g., the sadistic pathway in adult male perpetrators having adult female victims) and others are found in all categories of aggressors (e.g., escaping negative mood). These results support the view that internal (e.g., psychopathy, deviant sexual fantasies) and external (e.g., victim resistance) constraints interact in a limited number of ways, and so give rise to only a limited number of prototypical pathways to sexual aggression. • There is a limited number of pathways leading to the commission of a sexual assault. • Some pathways are specific to the sexual aggressors of women (sadistic and angry). • Some pathways are specific to the sexual aggressors of children (non-coercive deviant). • One pathway is common to all types of sexual aggressors (escaping negative affect).
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".