Cross-Validation of the Discrimination and Calibration Properties of the VRAG-R in a Treated Sexual Offender Sample
Bibliographic record
Abstract
The present study featured an examination of the predictive properties of the Violence Risk Appraisal Guide–Revised (VRAG-R) in a treated sample of sexual offenders, using modern risk metrics. The Sex Offender Risk Appraisal Guide (SORAG) and the original Violence Risk Appraisal Guide (VRAG) were examined for comparison purposes. The three measures were rated archivally on 296 treated sexual offenders followed up 17.6 years. VRAG-R scores demonstrated good discrimination of recidivists from nonrecidivists for sexual (area under the curve [AUC] = .60-.67) and violent (AUC = .70-.78) recidivism, and were incremental in the prediction of violent, but not sexual, recidivism after controlling for baseline sexual violence risk and treatment change. The VRAG-R bin structure demonstrated good calibration, although the present sample generated lower 5-year estimates of general violence compared with the normative sample. Application of the VRAG-R in the assessment and management of violence risk, via integration with dynamic risk assessment information, is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".