Using a General Case Management Tool With Partner-violent Men on Community Supervision in Iowa
Bibliographic record
Abstract
Intimate partner violence (IPV) is among the most common acts of violence against women worldwide, making it a major global threat to women's health and safety. The assessment and management of IPV offenders are therefore vital tasks in criminal justice systems. The current study examined whether the DRAOR, a general case management tool, was useful for supervising 112 male IPV offenders in Iowa, United States. Several risk factors emerged as potentially important treatment targets for partner-violent men, including poor attachment with others, substance abuse, anger/hostility, opportunity/access to victims, and problematic interpersonal relationships. While further research is needed to improve the utility of the DRAOR for predicting IPV recidivism, it assesses several factors that are relevant for supervising IPV cases (e.g., substance abuse, anger/hostility, victim access). This suggests the DRAOR could potentially be used to guide case management in the presence of a validated IPV tool that focuses on static risk factors, such as the ODARA. The use of the DRAOR with IPV offenders may also be warranted if they are found to be generally violent/antisocial rather than as family only offenders.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".