Predicting Recidivism in a High-Risk Sample of Intimate Partner Violent Men Referred for Police Threat Assessment
Bibliographic record
Abstract
It is unknown whether existing intimate partner violence (IPV) risk assessment tools can predict recidivism within threat assessment samples. We examined the predictive validity for IPV, any violent, and general recidivism of four commonly used IPV risk appraisal tools (Ontario Domestic Assault Risk Assessment [ODARA], Spousal Assault Risk Assessment version 2 [SARA-V2], SARA version 3 [SARA-V3], and Brief Spousal Assault Form for the Evaluation of Risk [B-SAFER]) with 247 men charged with IPV and referred to a threat assessment service. Total scores of the ODARA and SARA-V2—but not SARA-V3 or B-SAFER—significantly predicted IPV recidivism and any violent recidivism. The SARA-V2 Criminal History subscale and the B-SAFER subscale of “Past” events—but no other subscales of the SARA-V2, B-SAFER, or SARA-V3—significantly predicted IPV recidivism. Although effect sizes were smaller than in past research, our results support the use of the ODARA and SARA-V2 with threat assessment IPV populations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".