Policing beyond VAWIR policy : criminal harassment, pro-arrest and the practice of discretionary power
Bibliographic record
Abstract
Relevant policy dictates that only particular interventions are to be employed when police are handling incidents involving violence against women in relationships. The objective of this study is to examine the way police officers describe their practice when investigating cases involving women victims who are criminally harassed by intimate or formerly intimate partners. This study inquires into the influence of this policy as well as the influence of other situational and organizational factors in the specific area of policing criminal harassment. In-depth interviews took place with 20 Vancouver police officers and qualitative analysis was employed. The data shows that the officers in this sample see themselves as highly discretionary in their practice and that their decision-making is influenced by the way in which they construct a number of situational and organizational factors. It was found that the vast majority of police in this sample constructed the victim, the crime of criminal harassment, and the criminal justice system in such a way so as to justify not following the protocols outlined in the Violence Against Women in Relationships Policy. Furthermore, it is argued that the attitudes which inform their discretionary practice can be seen as reflective of the attitudes embedded in police subculture as well as in dominant society. The implications of this research point to a need for further exposure and, perhaps, parameters around police practice so as to limit the negative effects of the patriarchal police subculture on women victims of violence.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".