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Record W2134007102 · doi:10.1177/0261018311435025

Children’s best interests and intimate partner violence in the Canadian family law and child protection systems

2012· article· en· W2134007102 on OpenAlexafffundabout
Judy Hughes, Shirley Chau

Bibliographic record

VenueCritical Social Policy · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of British ColumbiaUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Northern British Columbia
KeywordsDomestic violencePrivilege (computing)Child protectionBest interestsFamily lawService (business)PsychologyChild custodyQualitative researchCriminologySuicide preventionPoison controlPolitical scienceSocial psychologyLawSociologyMedicineBusinessMedical emergency

Abstract

fetched live from OpenAlex

This article summarizes the findings of a project investigating women’s experiences with the Canadian child protection (CPS) and family law (FLS) systems. We examine both service systems together here because although both privilege children’s best interests as their primary consideration and define the concept similarly, the two systems diverge in their expectations of women relative to child custody. While FLS requires women to accept custody arrangements that provide close and continued contact between themselves and their former abusive partners, CPS expects women to leave these same abusive partners or risk removal of their children. The results of thirty-five qualitative interviews with women demonstrate their struggles, firstly, in having their experiences of intimate partner violence (IPV) recognized by professionals in the FLS, and, secondly, in becoming caught between the opposing expectations of CPS and FLS while not receiving help from either. Recommendations for change to improve these services are included in this article.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0430.024
Scholarly communication0.0090.003
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.041
GPT teacher head0.360
Teacher spread0.319 · 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 designQualitative
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

Citations33
Published2012
Admission routes3
Has abstractyes

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