Validation of and revision to the VRAG and SORAG: The Violence Risk Appraisal Guide—Revised (VRAG-R).
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it