A cross-validation of the Violence Risk Appraisal Guide—Revised (VRAG–R) within a correctional sample.
Bibliographic record
Abstract
The Violence Risk Appraisal Guide-Revised (VRAG-R) was developed to replace the original VRAG based on an updated and larger sample with an extended follow-up period. Using a sample of 120 adult male correctional offenders, the current study examined the interrater reliability and predictive and comparative validity of the VRAG-R to the VRAG, the Psychopathy Checklist-Revised, the Statistical Information on Recidivism-Revised, and the Two-Tiered Violence Risk Estimate over a follow-up period of up to 22 years postrelease. The VRAG-R achieved moderate levels of predictive validity for both general and violent recidivism that was sustained over time as evidenced by time-dependent area under the curve (AUC) analysis. Further, moderate predictive validity was evident when the Antisociality item was both removed and then subsequently replaced with a substitute measure of antisociality. Results of the individual item analyses for the VRAG and VRAG-R revealed that only a small number of items are significant predictors of violent recidivism. The results of this study have implications for the application of the VRAG-R to the assessment of violent recidivism among correctional offenders. (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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".