A Review of Domestic Violence Risk Instruments
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
The problem of domestic violence has been well documented with respect to its social, psychological, and economic costs. Proactive arrest and sentencing policies have resulted in an increasing, and in some cases, overwhelming number of spousal batterers being processed through the criminal justice system. Scarce correctional and treatment resources necessitate that difficult decisions be made about the management of domestic violence perpetrators. In an effort to make better decisions, many jurisdictions have adopted a risk assessment approach. Spousal assault risk assessment now serves to inform those making decisions about sentencing (e.g., community release vs. incarceration), treatment placement, and supervision intensity. With these developments, researchers and clinicians have begun to discuss the appropriate content and process of spousal assault risk assessment. There have been a number of efforts in recent years to develop theoretically and scientifically sound risk assessment instruments and procedures. This article attempts to review state-of-the-art instruments in this rapidly expanding field.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it