The Prediction of Violence in Adult Offenders
Bibliographic record
Abstract
Using 88 studies from 1980 to 2006, a meta-analysis compares risk instruments and other psychological measures on their ability to predict general (primarily nonsexual) violence in adults. Little variation was found amongst the mean effect sizes of common actuarial or structured risk instruments (i.e., Historical, Clinical, and Risk Management Violence Risk Assessment Scheme; Level of Supervision Inventory—Revised; Violence Risk Assessment Guide; Statistical Information on Recidivism scale; and Psychopathy Checklist—Revised). Third-generation instruments, dynamic risk factors, and file review plus interview methods had the advantage in predicting violent recidivism. Second-generation instruments, static risk factors, and use of file review were the strongest predictors of institutional violence. Measures derived from criminological-related theories or research produced larger effect sizes than did those of less content relevance. Additional research on existing risk instruments is required to provide more precise point estimates, especially regarding the outcome of institutional 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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".