Criminalizing Coercive Control in Canada: Learning from an International Comparative Analysis
Bibliographic record
Abstract
While coercive control and its role in family and intimate partner violence is not new, there has recently been an emerging movement toward its criminalization in various jurisdictions. This article does not argue that Canada should criminalize coercive control. Instead, given the recent interest in its criminalization, it simply examines how coercive control could be criminalized in Canada. This article begins by reviewing proposed theories and definitions of family violence, intimate partner violence, and coercive control. However, despite extensive literature on these topics, broad conclusions that can be drawn are limited, given the use of varying definitions and theoretical frameworks. Nevertheless, emerging empirical research has attempted to identify and measure coercive control’s key underlying constructs to standardize the operationalization of the term. This article examines this literature alongside legislation against coercive control from other jurisdictions to understand how coercive control could be better addressed legislatively in Canada. However, this article cautions against the likelihood that adding a new criminal offence on its own will have a meaningful effect in helping address the larger issues of family and intimate partner violence. Thus, this article concludes by offering three recommendations to ensure that a coercive control offence has its desired effect.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".