Doing More, Doing Better? A Critique of the Criminalization of Coercive Control
Bibliographic record
Abstract
Canada stands on the verge of ushering in a new criminal offence related to coercive control in the context of intimate relationships. In this paper we critically evaluate this development, drawing out lessons from past criminalization efforts and in particular, their impact on Black women and their communities. Our analysis raises significant questions about the potential of the proposed offence to deliver on the promises held out by its proponents. We argue that not only will these promises go largely unrealized, but the ongoing harms of criminalization–harms that manifest not only in the criminal law sphere but in child welfare and border control–will be intensified. Black and other marginalized women are the least likely to benefit, and the most likely to be harmed. While we argue against criminalizing coercive control, we maintain that it is essential that all legal system actors acquire a deep understanding of coercive control that attends to how multiple structures of oppression intersect to shape the tactics of coercive control, differentially distribute supports and resources, and limit the space for survivors to take action. Securing the safety of all women requires rooting out the deeply entrenched stereotypes of Black and other marginalized women and ensuring equitable access to vital supports and resources–among them, safe places to disclose the violence, affordable housing, a stable source of adequate income, and accessible transportation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.134 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.013 |
| 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".