The need for a Canadian Criminal Code offence of coercive control
Bibliographic record
Abstract
Canada is currently considering legislating an offence of coercive control. Coercive controlling behaviour is currently criminalized in the UK, Scotland, Ireland, Northern Ireland and New South Wales, Australia. Potential benefits of the implementation of a coercive control offence in Canada include enhancing victim/survivor safety with access to protective orders; allowing police to respond in situations where physical violence is not occurring and, importantly, respond in a way that is reflective of the type of violence being enacted and the assessed risk; moving beyond an incident-based view of intimate partner violence to recognize patterns; improving perpetrator accountability and opportunities for risk management; sending a clear message that these behaviours are unacceptable; enhancing public awareness of coercive control; bringing the Criminal Code in line with other recent legislation; and creating consistency between family and criminal courts. This article summarizes the concept of coercive control, including gendered implications and risks for domestic homicide; the need for a coercive control offence, including support from professionals; and guidance for the implementation of a coercive control offence, including promising practices from international legislation, risk assessment, training for police and other professionals, and evaluation and data gathering.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".