Risk Factors for Sexual Offenses Committed by Men With or Without a Low IQ: An Exploratory Study
Bibliographic record
Abstract
Although risk factors associated with offending and recidivism are relatively well-established for mainstream sexual offenses, much less is known about men with a low IQ who have sexually offended (MIQSO), let alone those with forensic involvement. In this exploratory study, 137 convicted for the commission of at least one sexual offense and found not criminally responsible because a mental disorder were recruited in a maximum-security hospital. They were all assessed with the SORAG (static risk factors) and the RSVP (dynamic risk factors). Compared with MIQSO (N = 76), men with an average or higher IQ who have sexually offended (MSO, N = 61) obtained significantly higher scores on static factors related with general delinquency (histories of alcohol abuse, non-violent criminality, violent criminality, and sexual offense) and dynamic factors related with sexual delinquency, paraphilia, and recidivism (chronicity, psychological coercion, escalation, sexual deviance, and substance abuse). In contrast, MIQSO obtained significantly higher scores on major mental illness, problems with planning and problems with self-awareness. Logistic regressions revealed that both the SORAG and RSVP were useful to predict group membership. It is concluded that risk factors related with general and sexual delinquency better describe offenses committed by MSO, whereas risk factors related with mental disorder, lack of insight and contextual impulsivity better describe offenses committed by MIQSO.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".