The validity of the Violence Risk Appraisal Guide (VRAG) in predicting criminal recidivism
Bibliographic record
Abstract
INTRODUCTION: The VRAG is an actuarial risk assessment instrument, developed in Canada as an aid to estimating the probability of reoffending by mentally ill offenders. AIM: To test the predictive validity of the VRAG with a German sample. METHOD: The predictive validity of the VRAG was tested on a sample of 136 people charged with a criminal offence and under evaluation for criminal responsibility in the forensic psychiatry department at the University of Munich in 1994-95. The predicted outcome was tested by means of ROC analysis for correlation with the observed rate of recidivism between discharge after the 1994-95 assessment and the census date of 31 March 2003. Recidivism rate was calculated from the official records of the National Conviction Registry. RESULTS: Just over 38% of the sample had reoffended by 2003. Their mean time-at-risk was 58 months (SD 3.391; range 0-115 months). The VRAG yielded a high predictive accuracy in the ROC analysis with an AUC of 0.703. For a constant time-at-risk < = 7 years, the predicted probability and observed rates of recidivism correlated significantly with Pearson's r = 0.941. CONCLUSIONS: The validity of the VRAG was replicated with a German sample. The VRAG yielded good predictive accuracy, despite differences in sample and outcome variables compared with its original sample.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".