The validity of violence risk estimates: An issue of item performance.
Bibliographic record
Abstract
Typically, research conducted on the cross-validation or generalization of risk assessment schemes focuses on the aggregate score accuracy of the schemes within the new sample or population. Often overlooked when the schemes are examined in their aggregate form is the performance of the individual items. This study looks at the association between the items of the HCR-20 (C. D. Webster, K. S. Eaves, D. Douglas, & S. D. Wintrup, 1995) and the Violence Risk Appraisal Guide (VRAG;C. D. Webster, G. T. Harris, M. E. Rice, C. Cormier, & V. L. Quinsey, 1994) and violent recidivism in a sample of predominantly violent offenders. The results show that a number of the items from each scale do not distinguish between violent recidivists and nonrecidivists and that the presence of these items potentially reduces the predictive accuracy of the instruments. In addition, the inclusion of items that do not discriminate between recidivists and nonrecidivists potentially undermines the validity of the risk assessment process. Discussion centers on the application of prediction schemes and their individual risk factors in forensic practice.
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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 | 0.564 | 0.728 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".