Bath Institution The predictive accuracy of the Psychopathy Checklist–Revised, Level of Service Inventory– Revised, HCR-20, Violence Risk Appraisal Guide, and the Lifestyle Criminality Screening
Bibliographic record
Abstract
Form were compared in a sample of male offenders. Both correlations and receiver operating characteristics measured the relationship between the instruments and the predictive outcome criteria of institutional misconduct and release failure. Although some instruments performed better across the outcome measures, there were no statistical differences in predictive accuracy among the instruments. Classification of persons and prediction of behaviors are funda-mental goals in forensic settings, and much effort has been spent on pursuing these goals. The attainment of these goals leads to equita-ble judgments of clients and a more efficient administration of the criminal justice system. Soon after an individual comes into contact with the criminal justice system, there are decision points where an 471 AUTHORS ’ NOTE: Gratefully acknowledged are Roberto Di Fazio and Jeanne Clark for their assistance with the study. Also acknowledged are John Reddon, Adelle Forth, and Ralph Serin for their comments. The views expressed in this article are those of the authors and do not necessarily reflect the views of the Correctional Ser-vice of Canada. Correspondence should be addressed to Daryl G. Kroner, Pittsburgh
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How this classification was reachedexpand
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".