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Record W2149099761 · doi:10.1177/1557085113480824

Canadian News Coverage of Intimate Partner Homicide

2013· article· en· W2149099761 on OpenAlexafffundabout
Jordan Fairbairn, Myrna Dawson

Bibliographic record

VenueFeminist Criminology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of GuelphCarleton University
FundersCanada Research Chairs
KeywordsHomicideNewspaperIntimate partnerDomestic violenceCriminologyPoliticsPoison controlSuicide preventionMedia coverageFemicideHuman factors and ergonomicsPsychologySocial psychologyPolitical scienceSociologyMedicineLawEnvironmental healthMedia studies

Abstract

fetched live from OpenAlex

News coverage of intimate partner homicide can reveal and reproduce societal assumptions and beliefs that may influence social and political responses to violence against women. This study analyzes all male-perpetrated intimate partner homicides reported in three daily newspapers in Toronto, Canada within two separate time periods (1975-1979 and 1998-2002) to explore if and how this coverage has changed over time. Results suggest that, in more recent years, news coverage is more likely to report a previous history of intimate partner violence and less likely to employ news that excuses or justifies the perpetrator’s actions. However, coverage continues to employ victim-blaming news frames and to portray intimate partner homicide as an individual event, in part, through the absence of the voices of violence against women organizations, researchers, and service providers as legitimate authorities in both time periods. Thus, news coverage fails to encourage social and political responses to violence against women in intimate relationships that emphasize the need for social structural changes focusing on gender equality.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0130.020
Science and technology studies0.0030.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.001

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 head0.051
GPT teacher head0.318
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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

Citations84
Published2013
Admission routes3
Has abstractyes

Explore more

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