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Record W2114472933 · doi:10.1177/0886260507307914

The Elevated Risk for Non-Lethal Post-Separation Violence in Canada

2007· article· en· W2114472933 on OpenAlexaffabout
Douglas A. Brownridge, Ko Ling Chan, Diane Hiebert‐Murphy, Janice Ristock, Agnes Tiwari, Wing-Cheong Leung, Susy Santos

Bibliographic record

VenueJournal of Interpersonal Violence · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsJealousyDomestic violenceContext (archaeology)DemographyPoison controlSeparation (statistics)PsychologySuicide preventionInjury preventionSexual violenceOccupational safety and healthHuman factors and ergonomicsMedicineSocial psychologyMedical emergencySociologyGeographyCriminology

Abstract

fetched live from OpenAlex

The purpose of the study was to shed light on the potentially differing dynamics of violence against separated and divorced women by their ex-husbands and violence against married women by their current husbands. Using a nationally representative sample of 7,369 heterosexual women from Cycle 13 of Statistics Canada's General Social Survey, available risk markers were examined in the context of a nested ecological framework. Separated women reported nine times the prevalence of violence and divorced women reported about four times the prevalence of violence compared with married women. The strongest predictors of violence against married women, namely, patriarchal domination, sexual jealousy, and possessiveness, were not significant predictors of violence against separated and divorced women. This suggested that post-separation violence is a complex phenomenon the dynamics of which can be affected by much more than domination and ownership.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.016
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.012
GPT teacher head0.319
Teacher spread0.308 · 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

Citations105
Published2007
Admission routes2
Has abstractyes

Explore more

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