“Weaponizing” The Tort of Family Violence? Myths, Stereotypes, Lawyers’ Ethics and Access to Justice
Bibliographic record
Abstract
Intimate partner violence [IPV] causes myriad and gendered harms, but Canadian law has inconsistently provided avenues of economic redress. Although tort law has evolved to allow IPV survivors to seek compensation, tort-based remedies are sought rarely and largely limited to intentional torts such as assault, battery, and the intentional infliction of emotional distress. These torts do not always encompass the harms sustained by IPV survivors, particularly those caused by economic abuse and coercive control. In Ahluwalia v Ahluwalia, a 2022 family law case, Justice Renu Mandhane responded to this gap in the law by recognizing a new tort of family violence, but her decision was overturned by the Ontario Court of Appeal in 2023, and the case is now before the Supreme Court of Canada. Our paper provides a feminist analysis of the role of tort law in providing compensatory remedies for survivors of IPV. We situate tort remedies and Ahluwalia within the wider context of Canadian laws addressing IPV and feminist critiques of tort law and theory. This wider context raises issues about access to justice and socio-economic responses to IPV for members of marginalized groups in particular. We also examine how myths and stereotypes have influenced this area of law and the role of lawyers and judges in this respect, including in Ahluwalia. We conclude that recognition of the tort of family violence is an important but limited step forward in compensating the harms of IPV, and we urge governments to do more to systemically remediate these harms.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.132 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| 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".