Predicting Recidivism in Sex Offenders Using the VRAG and SORAG: The Contribution of Age-at-Release
Bibliographic record
Abstract
Sex offenders (N = 468) were released from custody and recidivism outcome recorded. The Violence Risk Appraisal Guide (VRAG) and Sex Offender Risk Appraisal Guide (SORAG) were scored for each offender. Results indicated that, for the majority of actuarial items contained in the VRAG and SORAG and for VRAG and SORAG bin scores, offenders with lower actuarial scores were released from custody at a significantly older age. Actuarial scores were regressed on age-at-release and residuals saved as age-corrected actuarial scores. Using ROC analysis, predictive accuracy was evaluated comparing original actuarial scores with age-corrected scores. For most item scores and both bin scores, the ability to predict recidivism was significantly reduced after the effects of age-at-release had been removed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".