Predicting Violence by Serious Wife Assaulters
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
Assessing violence risk among wife assaulters is receiving increasing attention in the literature, but risk assessment tools specifically for this population are just beginning to be developed. The literature on wife assaulters suggests the importance of antisocial personality and behavior. The present study examines psychopathy; the Violence Risk Appraisal Guide (VRAG), a validated actuarial risk assessment tool for violent recidivism; and motives thought to be related to wife assault, in predicting violent recidivism among 88 men with a history of serious wife assault. Violent recidivism was lower among wife assaulters (24%) than among a larger sample of generally violent offenders (44%). Score on the Hare Psychopathy Checklist-Revised was a good predictor of subsequent violence, r = .35, and score on the VRAG was a significantly better predictor, r = .42, area under the curve (AUC) = .75. The prospects for predicting lethal wife assault and violence against specific victims are discussed.
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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