Reformulating the Use of Battered Woman Syndrome Testimonies in Canadian Law
Bibliographic record
Abstract
This article examines the use of battered woman syndrome (BWS) expert testimonies in Canadian case law, regarding cases involving murder or attempted murder of abusive partners by women in violent intimate relationships. The purpose of this article is to contribute to literature about the use of BWS evidence in Canadian jurisprudence with connections to social work. The author provides a historical overview of the use of BWS testimonies in Canada and presents case examples. The article explores the benefits of BWS testimonies, its limitations, recommendations for reformulating its use, and implications for social work practice.
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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 | 0.032 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.025 | 0.027 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| 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".