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Record W7096700230

10.1177/1077801204265017ARTICLEVIOLENCE AGAINST WOMEN / June 2004Brownridge / VIOLENCE IN COMMON-LAW UNIONS Understanding Women’s Heightened Risk of Violence in Common-Law Unions Revisiting the Selection and Relationship Hypotheses

2016· article· en· W7096700230 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsnot available
Fundersnot available
KeywordsCohabitationSelection (genetic algorithm)Marital statusPersistence (discontinuity)Sample (material)Parity (physics)Poison control
DOInot available

Abstract

fetched live from OpenAlex

Cohabiting women’s heightened risk of violence is a well-established relationship but one that is not fully understood. Using a recently collected representative sample of 7,396 Canadian women, the results of the present study show that the cohabitation-violence relationship persists. However, over a 6-year period, the prevalence of violence against women in cohabiting unions declined dramatically relative to marital unions. The results of the analyses suggest that the increasing prevalence of cohabitation has reduced the selection bias, in turn causing cohabitors ’ and “marrieds” ’ relationships to become increasingly similar. Nevertheless, the results also suggest that cohabitors remain a select group, and this differential selection is responsible for the persistence of their higher like-lihood of violence. Overall, the results suggest that cohabitation will need to become much more prevalent before parity in rates of violence will be achieved between cohabitors and marrieds in Canada.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.9250.703

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 head0.040
GPT teacher head0.271
Teacher spread0.231 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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