Comparative Validity Analysis of Multiple Measures of Violence Risk in a Sample of Criminal Offenders
Bibliographic record
Abstract
This study compared the predictive validity of multiple indices of violence risk among 188 general population criminal offenders: Historical-Clinical-Risk Management-20 (HCR-20) Violence Risk Assessment Scheme, Violence Risk Appraisal Guide (VRAG), Violent Offender Risk Assessment Scale (VORAS), Hare Psychopathy Checklist-Revised (PCL-R), and Screening Version (PCL:SV). Several indices were related to violent recidivism with large statistical effect sizes: HCR-20 (Total, Clinical and Risk Management scales, structured risk judgments), VRAG, and behavioral scales of psychopathy measures. Multivariate analyses showed that HCR-20 indices were consistently related to violence and that the VRAG entered some analyses. Findings are inconsistent with a position of strict actuarial superiority, as HCR-20 structured risk judgments—an index of structured professional or clinical judgment—were as strongly related to violence.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".