Do psychological features distinguish those who sexually offend against different age groups from those who are stable in victim age?
Bibliographic record
Abstract
Aim/Background Victim age polymorphism occurs when someone offends against victims that span multiple age groups (e.g., child and adult victims). There is a need to better understand the correlates of age polymorphism, as clinicians are often asked about risk of offending against victims who may differ from the index offence victim as part of their risk formulation. The present study examines several potential correlates of age polymorphism: psychopathy, sexual preoccupation, multiple paraphilias, psychosis, and substance use disorders. Materials/Method Analyses were conducted using secondary clinical assessment data from a provincial forensic sexual behaviour program. The sample included 387 men with two or more contact sexual offence victims. The assessment data in the archival database included comprehensive information about victim age, as well as standardized assessment measures and diagnostic/clinical impressions. Results There were no significant associations between age polymorphism and psychopathy, multiple paraphilias, sexual preoccupation, psychosis, and substance use disorders. The only significant difference that emerged was that men who offended against victims 16 or older had a higher mean score on a measure of drug misuse than those who offended against victims 6 to 11. Most of the analyses produced small effects. Conclusion Our findings did not identify significant correlates of age polymorphism when restricting analyses to those men who offended against two or more victims. We consider key methodological differences that may have impacted our findings, as well as the need for rigorously designed research to develop a comprehensive model of age polymorphism.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".