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Record W2985485314 · doi:10.15173/ijrr.v2i2.3820

Battered woman syndrome

2019· article· en· W2985485314 on OpenAlexaffabout
Graham Glancy, Marissa Heintzman, A. M. Wheeler

Bibliographic record

VenueInternational Journal of Risk and Recovery · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsChecklistDutyPsychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.287
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes2
Has abstractyes

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