Actuarial risk assessment of sexual offenders: The psychometric properties of the Sex Offender Risk Appraisal Guide (SORAG).
Bibliographic record
Abstract
The Sex Offender Risk Appraisal Guide (SORAG) is one of the most commonly used actuarial risk assessment instruments for sexual offenders. The aims of the present field study were to examine the predictive validity of the German version of the SORAG and its individual items for different offender subgroups and recidivism criteria in sexual offenders released from the Austrian Prison System (N = 1,104; average follow-up period M = 6.48 years) within a prospective-longitudinal research design. For the prediction of violent recidivism the German version of the SORAG yielded an effect size of AUC = .74 (p < .001, 95% CI = .70-.78). The predictive accuracy for general and violent recidivism was slightly higher than for general sexual and sexual hands-on recidivism. The effect sizes were found to be higher for the child molester sample than for rapists. However, the differences were significant only for general recidivism (z = 2.48, p = .001). Further analyses exhibited the SORAG to have incremental predictive validity beyond the VRAG and the PCL-R, and to remain the only significant predictor for violent recidivism once all 3 instruments were forced into a combined regression model. Twelve out of the 14 SORAG items were found to have a significant positive relationship with violent recidivism. The comparison of the relative and absolute risk indices between the Austrian and the Canadian samples showed that the normative data distribution yielded more (absolute risk indices) or less (relative risk indices) meaningful differences between the 2 countries. (PsycINFO Database Record
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".