Prospective replication of the Violence Risk Appraisal Guide in predicting violent recidivism among forensic patients.
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
An exhaustive survey of a cohort of forensic patients provided an opportunity for a prospective replication of the predictive accuracy of the Violence Risk Appraisal Guide (VRAG). Data collected during the original survey also permitted a test of the predictive accuracy of clinical assessments of risk on the same cohort. The VRAG yielded a large effect size in predicting violent recidivism (ROC area = .80) over a constant 5-year follow-up and performed significantly better than averaged clinical opinions. The superiority of the VRAG was also observed at very short follow-up times and for very serious violence. Moreover, for 16 subsamples, observed rates of violent recidivism did not differ significantly from the expected rates. VRAG score was unrelated, and clinical judgments inversely related to violent recidivism in the small low-risk sample of female forensic patients. The authors conclude that, regardless of length of opportunity or severity of outcome, actuarial methods are more accurate than is clinical judgment.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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