Bibliographic record
Abstract
Given that sexual offenders are more likely to reoffend with a nonsexual offense than a sexual offense, it is useful to have risk scales that predict general recidivism among sexual offenders. In the current study, we examined the extent to which two commonly used risk scales for sexual offenders (Static-99R and Static-2002R) predict violent and general recidivism, and whether it would be possible to improve predictive accuracy for these outcomes by revising their items. Based on an aggregated sample of 3,536 adult male sex offenders from Canada, the United States, and Europe (average age of 39 years), we found that a scale created from the Age at Release item and the General Criminality subscale of Static-2002R predicted nonsexual violent, any violent, and general recidivism significantly better than Static-99R or Static-2002R total scores. The convergent validity of this new scale (Brief Assessment of Recidivism Risk-2002R [BARR-2002R]) was examined in a new, independent data set of Canadian high-risk adult male sex offenders (N = 360) where it was found to be highly correlated with other risk assessment tools for general recidivism and the Psychopathy Checklist-Revised (PCL-R), as well as demonstrated similar discrimination and calibration as in the development sample. Instead of using total scores from the Static-99R or Static-2002R, we recommend that evaluators use the BARR-2002R for predicting violent and general recidivism among sex offenders, and for screening for the psychological dimension of antisocial orientation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.402 | 0.143 |
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".