The Role of Police Risk Assessments in Judicial Decisions Regarding Domestic Violence Offenses in Israel
Bibliographic record
Abstract
Increasing awareness to the issue of domestic violence offenses in Israeli society has led to changes in legislation and enforcement, with an additional degree of severity attributed to domestic violence. These changes have also led the Israel Police to develop an actuarial risk assessment tool to improve the validity of decision-making processes regarding domestic violence. The purpose of this tool was to empower police investigators to assess information from domestic violence complaints, and to derive the best recommended action from that information. Thus, the tool results in uniformity of attitude between various professionals in the law-enforcement system towards domestic violence. To test whether this tool indeed increases uniformity of attitude between various law-enforcement professionals, towards the risk level of the assaulting partner, this study examined all domestic violence offense cases opened in a large city in the south of Israel and analyzed a small sample of protocols from domestic violence investigations that ended with conviction. The study data show that both in requests for remand extension and in penalty judgment decisions, the legal system tends to ignore the risk assessment score provided by the police tool. These data indicate that Israeli legal discourse tends to overlook police risk assessments of domestic violence offenders, which in theory could increase the probability of “false negative” errors in predicting the risk level of an offender. In turn, this may result in additional assaults by violent partners against their victims.
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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 | 0.029 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".