Does Training Matter? Exploring Police Officer Response to Domestic Dispute Calls before and after Training on Intimate Partner Violence
Bibliographic record
Abstract
Police officers often leave the scene of many domestic dispute calls, unable to collect evidence needed to lay charges against the accused. They may find the residence in order, no visible signs of injury, and denial/refusal of all parties to provide statements to the police. Police officers may be called to respond to couples with repeated calls to emergency response, leaving without evidence on numerous occasions. As a result, many police officers may go to calls with preconceived notions about the risks and dynamics in intimate partner relationships, potentially impacting the kind of intervention used. Based on analysis of 1,032 domestic dispute files in Fredericton, NB, this paper explores police officer intervention before and after training on responding to intimate partner violence. It compares formal intervention, such as charges and arrests; collection of evidence, including written and oral statements from victim(s) and witness(es); and informal strategies used by police officers who respond to the scene of a domestic dispute, such as temporary separation of victim and accused, providing transportation to another residence, and contacting a shelter on behalf of the victim or accused.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".