CRIMINAL PROPENSITY, DEVIANT SEXUAL INTERESTS AND CRIMINAL ACTIVITY OF SEXUAL AGGRESSORS AGAINST WOMEN: A COMPARISON OF EXPLANATORY MODELS*
Bibliographic record
Abstract
Three hypotheses have been used to describe the male propensity for sexual aggression towards women: a general propensity to offend, a specific propensity to sexually offend and a combination of both. In this paper, using structural equation modeling, we compared the relative utility of these three hypotheses in explaining criminal activity in adulthood of sexual aggressors of women. In total, 209 adult males who were convicted of at least one sexual offence were included in the study. Results indicate that a propensity model emphasizing the role of an early and persistent general propensity to act in an antisocial manner during childhood and adolescence is most adequate to explain sexual aggressors' criminal activity. After controlling for the role of this propensity, a specific propensity characterized by high sexualization and deviant sexual interests explained only a modest proportion of variance of the sexual criminal activity.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".