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Record W1982705470 · doi:10.7202/043935ar

Defending Victims of Domestic Abuse who Kill : A Perspective from English Law

2005· article· en· W1982705470 on OpenAlexvenueno aff
George Mousourakis

Bibliographic record

VenueLes Cahiers de droit · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicHomicide, Infanticide, and Child Abuse
Canadian institutionsnot available
Fundersnot available
KeywordsProvocation testRelevance (law)Diminished responsibilityLawPerspective (graphical)English lawCommissionCriminal lawScope (computer science)Relation (database)Political scienceCriminologySociologyMedicine

Abstract

fetched live from OpenAlex

The term “cumulative provocation” is used to describe cases involving a prolonged period of maltreatment of a person at the hands of another, which culminates in the killing of the abuser by her victim. Since the early 1990s there has been a plethora of academic commentary on the criminal law’s response to such cases. More recently, the debate has been re-opened following the publication of the English Law Commission’s proposals on the partial defences to murder. This article examines doctrinal issues that arise in relation to claims of extenuation stemming from the circumstances of cumulative provocation. It is argued that, given the scope and limitations of the provocation defence, one should view the circumstances of cumulative provocation as likely to bring about the conditions of different legal excuses. Identifying the relevant legal defence would require one to reflect on the nature of the excusing condition or conditions stemming from the circumstances of each particular case. Although the paper draws largely upon the doctrines of provocation and diminished responsibility as they operate in English law, it is hoped that the analysis offered has relevance to all systems where similar defences are recognized (or proposed to be introduced), and can make a useful contribution to the continuing moral debate that the partial excuses to murder generate.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.995

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 studies0.0000.001
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 head0.006
GPT teacher head0.252
Teacher spread0.246 · 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 designQualitative
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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueLes Cahiers de droitSame topicHomicide, Infanticide, and Child AbuseFrench-language works237,207