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Record W2080712246 · doi:10.1177/0004865812456850

Intimacy, homicide, and punishment: Examining court outcomes over three decades

2012· article· en· W2080712246 on OpenAlexaffabout
Myrna Dawson

Bibliographic record

VenueAustralian & New Zealand Journal of Criminology · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHomicidePleaJurisdictionCriminologyIntimate partnerDomestic violencePunishment (psychology)Poison controlPsychologySuicide preventionPolitical scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

Little is known about changing patterns in official responses to crime over time despite changes in recent decades in how the law treats various types of violent crime. Drawing from data documenting court outcomes in homicides in one Canadian urban jurisdiction from 1974 to 2002, this study examines the role played by intimacy in law during several distinct social and public policy periods in one country. The central hypothesis is that the accused in intimate partner homicides will be subject to ‘less law’ than those in non-intimate partner homicides. However, given social and policy transformations, it is further hypothesized that evidence of differential treatment should be less in recent years and, specifically, post-Bill C-41 which was meant to change the way intimacy was considered at sentencing. In examining multiple decision points, results show that while differential treatment of intimate partner and non-intimate homicide was evident at some stages, it was not always in the direction hypothesized. Further, while patterns in treatment did change over time with ‘more law’ evident in cases involving intimate partners in more recent years, plea resolutions remained more common for intimate partner killers than for those who killed victims with whom they shared more distant relationships.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.170
GPT teacher head0.395
Teacher spread0.225 · 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.

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

Citations16
Published2012
Admission routes2
Has abstractyes

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