Antisociality in intimate partner violence risk assessment: an analysis of the SARA-V2, SARA-V3, and B-SAFER
Bibliographic record
Abstract
Purpose There is evidence that risk factors measuring aspects of antisociality contribute substantially to the prediction of recidivism in actuarial intimate partner violence (IPV) risk assessment tools, however, this has not been examined in structured professional judgment (SPJ) IPV risk assessment tools. This study aims to examine the representation and predictive accuracy of factors measuring antisociality in three SPJ tools. Design/methodology/approach The authors investigated items measuring antisociality in the SARA-V2, SARA-V3 and B-SAFER to assess IPV risk in a Canadian sample of 266 men who had previously committed a violent crime against their female partner. The authors examined whether the underlying factor structure empirically separated antisocial items from other items and analyzed the predictive accuracy of the antisocial factors. Findings Partial antisocial factors emerged in the SARA-V2 and SARA-V3, while a clear antisocial factor emerged in the B-SAFER. All antisocial factors demonstrated significant predictive values for IPV recidivism in area under the curve and Cox regression analyses. Originality/value This study offers a novel contribution to the field by using an empirical approach to examine antisocial factors in three commonly used SPJ tools. The findings underscore the need for a continued focus on antisocial traits and behaviors during IPV risk estimation, management and treatment.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".