Validation of and revision to the VRAG and SORAG: The Violence Risk Appraisal Guide—Revised (VRAG-R).
Bibliographic record
Abstract
The violence risk appraisal guide (VRAG) was developed in the early 1990s, and approximately 60 replications around the world have shown its utility for the appraisal of violence risk among correctional and psychiatric populations. At the same time, authorities (e.g., Dawes, Faust, & Meehl, 1989) have argued that tools should be periodically evaluated to see if they need to be revised. In the present study, we evaluated the accuracy of the VRAG in a sample of 1,261 offenders, fewer than half of whom were participants in the development sample, then developed and validated a revised and easier-to-score instrument (the VRAG-R). We examined the accuracy of both instruments over fixed durations of opportunity ranging from 6 months to 49 years and examined outcome measures pertaining to the overall number, severity, and imminence of violent recidivism. Both instruments were found to predict dichotomous violent recidivism overall and at various fixed follow-ups with high levels of predictive accuracy (receiver operating characteristic areas of approximately .75) and to significantly predict other violent outcomes.
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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 | 0.040 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".