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.
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.
How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".