What Works in Assessing Recidivism Risk in People Convicted of Intimate Partner Violence
Bibliographic record
Abstract
Intimate partner violence (IPV) is a form of gender-based violence. IPV can occur within the context of a marital or cohabitating relationship and can extend beyond into other forms of relationships, such as dating relationships without cohabitation. This chapter provides an overview of research and evidence-informed practice in assessing recidivism risk among persons convicted of IPV offences. It presents a definitional overview of IPV risk assessment, its context and prevention-based functions per the risk–need–responsivity model. The chapter discusses the issues and approaches to IPV risk assessment, including recidivism base rates, factors associated with IPV and risk tool classification and implementation. It focuses on assessment considerations in research and practice, including linking assessment and intervention, evidence supporting the dynamism and manageability of IPV and applications to ethnocultural minorities. The chapter also provides a series of recommendations for IPV risk assessment.
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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 | 0.033 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".