The antisocial attitudes and associates of sex offenders
Bibliographic record
Abstract
INTRODUCTION: Meta-analyses have demonstrated that attitudes and associates (peer group behaviour) are among the best predictors of antisocial behaviour in offender populations. Research on sex offender attitudes has typically focused on sex-related content and not antisocial attitudes in general. This study investigates the antisocial attitudes of sex offenders by comparing them with non-sex offenders on responses to the Measures of Criminal Attitudes and Associates (MCAA). METHOD: The MCAA comprises two parts. Part A is a quantified self-report measure of criminal friends. Part B contains four attitude scales: Violence, Entitlement, Antisocial Intent and Associates. Ninety sex offenders were compared with 119 nonsex offenders on their endorsement of the MCAA and criminal history. RESULTS: Sex offenders endorsed fewer antisocial attitudes, reported fewer criminal friends and had fewer incarcerations than did non-sex offenders. Rapists endorsed antisocial attitudes more than did child molesters and incest offenders. However, these differences disappeared on controlling for age. A finding of fewer previous incarcerations among sex offenders was robust even controlling for age. CONCLUSION: The MCAA appears to be a reliable and valid instrument with sex offender samples. General antisocial attitudes appear to have a similar relationship with criminal history for both sex offenders and non-sex offenders, and should not be ignored in future studies or clinical practice. While a general sense of entitlement was not associated with sex offending per se, its stronger association with incarceration among sex offenders than non-sex offenders might suggest that this has the potential for identifying an important, perhaps more serious sub-group of sex 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".