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Record W2467059502 · doi:10.1057/9780230596665_2

Contextualising Domestic Homicides

2001· book-chapter· en· W2467059502 on OpenAlexaff
Wendy Chan

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2001
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHomicide, Infanticide, and Child Abuse
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCriminologyHomicideCriminal justiceStereotype (UML)Economic JusticeSubject (documents)Political scienceLawPsychologySociologyPoison controlSuicide preventionSocial psychologyMedicine

Abstract

fetched live from OpenAlex

Traditionally, the act of murder has been viewed as a crime committed by men. The subject of women’s violent crimes has received only cursory attention in past academic research, and media attention typically focuses on high-profile or sensational cases such as those of Sara Thornton and Kiranjit Ahluwahlia. As a result, there has been a tendency in discussions about women’s acts of murder to view their crimes as an aberration or to stereotype women who kill as inherently evil. In doing so, they are no longer viewed as ordinary women, but are now categorized as violent offenders. This has led to allegations of discriminatory treatment of women murderers in the legal system. Whilst scattered non-feminist research has been conducted in this area, it has not offered an analysis of the treatment of women murderers in the criminal justice system. It was not until the mid-1970s that feminists’ research in this area became significant and began to address the issue of legal discrimination against female homicide defendants. Yet, in an attempt to understand the actions and motivations of women in England who kill, writers have had to rely on the few feminist studies emanating primarily from America and Australia. The absence of a detailed analysis highlighting the circumstances of women who kill their partners in England has arguably hindered the debate about the legal treatment of women murderers in England.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.026
GPT teacher head0.283
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2001
Admission routes1
Has abstractyes

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