“When Something Happens to You, We’ll Do Something”: Coercive Control Survivors Seeking Help from Canadian Law Enforcement
Bibliographic record
Abstract
Coercive control is a pattern of behavior designed to exploit, isolate, control, microregulate, and dominate victims, and the prevalence of this phenomenon in intimate partner violence (IPV) situations is gaining global attention. Police officers continue to be a primary resource for victims of IPV when they seek help; however, they often report dissatisfaction with law enforcement. Interviews were conducted with survivors of IPV across Canada between 2019–2020 to learn more about their help-seeking behaviors and responses they found supportive or unhelpful. Utilizing a thematic analysis approach, secondary data analysis was conducted with a sample of 21 transcripts from survivors across all regions of Canada who had described their encounters with police officers following reports of abuse. It was hypothesized that the type of abuse experienced (incident-specific physical abuse vs. coercive control) would have an impact on the intervention and response they received. Findings suggest that survivors who experienced physical violence that contained clear evidence and/or injury were met with a swift, positive police response; however, those who reported ongoing patterns of abuse that were more covert were more often met with dismissal and a lack of urgency. This paper suggests that while coercive control is not currently a criminal code offense in Canada, police will continue to be solicited for support by victims. Therefore, updated police training is required to ensure that police officers understand the complexity of IPV and are not responding to calls for service with restricted views.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.032 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".