The Prediction of Violence in Adult Offenders
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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