Sexual knowledge and attitudes of men with intellectual disability who sexually offend
Bibliographic record
Abstract
BACKGROUND: Various explanations of sexual offending in men with intellectual disability (ID) have stressed sexual deviance and a lack of developmental socio-sexual knowledge. METHOD: Using the normative dataset of people with ID from the development of the Socio-Sexual Knowledge and Attitudes Assessment Tool - Revised (SSKAAT-R: Griffiths & Lunsky, 2003), two samples of individuals with ID and a history of sexual offence were compared on sexual knowledge to matched samples of individuals with ID and no known sexual offences. RESULTS: Offenders with ID who were identified as having engaged in sexually inappropriate behaviour, such as public masturbation or touching someone inappropriately, did not differ in terms of sexual knowledge from their matched sample of individuals with ID with no sexual offence history, whereas offenders who had committed more serious offences demonstrated greater sexual knowledge than matched non-offenders. When only those individuals who had received prior sex education were compared in terms of sexual knowledge, there were no differences between groups. However, sex offenders (serious offences) expressed more liberal attitudes than sex offenders (inappropriate behaviour) and non-offenders towards same-sex activities. CONCLUSIONS: The study points to the dynamic effect of socio-sexual education on offenders' knowledge and attitudes, and highlights potential differences in the knowledge and attitudes of different subtypes of offenders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".