Interrater reliability of Violence Risk Appraisal Guide scores provided in Canadian criminal proceedings.
Bibliographic record
Abstract
Published research suggests that most violence risk assessment tools have relatively high levels of interrater reliability, but recent evidence of inconsistent scores among forensic examiners in adversarial settings raises concerns about the "field reliability" of such measures. This study specifically examined the reliability of Violence Risk Appraisal Guide (VRAG) scores in Canadian criminal cases identified in the legal database, LexisNexis. Over 250 reported cases were located that made mention of the VRAG, with 42 of these cases containing 2 or more scores that could be submitted to interrater reliability analyses. Overall, scores were skewed toward higher risk categories. The intraclass correlation (ICCA1) was .66, with pairs of forensic examiners placing defendants into the same VRAG risk "bin" in 68% of the cases. For categorical risk statements (i.e., low, moderate, high), examiners provided converging assessment results in most instances (86%). In terms of potential predictors of rater disagreement, there was no evidence for adversarial allegiance in our sample. Rater disagreement in the scoring of 1 VRAG item (Psychopathy Checklist-Revised; Hare, 2003), however, strongly predicted rater disagreement in the scoring of the VRAG (r = .58). (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 | 0.029 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".