Bibliographic record
Abstract
This paper analyzes the exclusion of responsibility in cases of battered women who kill their abusive partners in self-defence, emphasizing the theoretical and practical difficulties from a gender lens. I investigate self-defence simultaneously from a double perspective: the perspective of intimate partner violence, and the perspective of Canadian law. I reflect on alternative solu-tions in cases where there was a deferred self-defence, seeking a more equitative response from institutions. Self-defence protects whoever kills another person to defend herself or a third party. Even though this legal figure seems unquestionable, it is actually an ambiguous area in criminal law. Women who are abused for extended periods of time, who one day kill their abu-sers, generally do not do so during a context of physical confrontation. In this paper, instead of merely restricting my analysis to the events that occurred on the day of the abuser’s death, I will go back in time to scrutinize in detail the cycle of systematic violence and the “battered woman syndrome”, as well as the theory of “coercive control” in the Canadian context. I draw from the famous Canadian case Rust v. Lavallee (1990). I problematize some of the requirements for self-defence, emphasizing their inability to respond to the realities of battered women. This research shows a problematic disconnect between the current legal framework and the realities of violence against women.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".