Bibliographic record
Abstract
The aim of this article is to examine the current state of the battered woman syndrome (BWS) defence in Canada and propose an update to the list of factors considered by experts evaluating the applicability of the defence to individual cases. The history and current legal definition of the defence are presented, and theories relating to BWS are summarized. Factors required of expert testimony in BWS cases are presented; cases relevant to the development of the defence that highlight these assessment factors are discussed. In a subsequent section, limitations of the defence and the role of the expert are explored. The PTSD Checklist (used in clinician diagnosis) is summarized before an updated, BWS-specific expert checklist is proposed. The updated checklist proposes six elements to be considered by an expert assessing a BWS case: 1. environmental factors, 2. attempts to leave or alter the situation, 3. risk factors of the abuser, 4. risk factors of the victim, 5. triggers for violence, and 6. contrary evidence. It is hoped that using this checklist will help experts to cover all the essential elements they must consider in order to conclude that a woman satisfies the criteria for BWS. In particular, this updated checklist will help experts to prepare comprehensive testimony that addresses the five issues defined by Justice Wilson as the expert’s duty to assess. In addition, this checklist will help experts present a firm foundation for a defence regarding the critical question of why the night of the offence was different from all other nights.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".