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Record W2169894209 · doi:10.1177/1466138108099591

The civil restraining order application process

2009· article· en· W2169894209 on OpenAlexaff
Jill Adams

Bibliographic record

VenueEthnography · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsOrder (exchange)EthnographySociologyProcess (computing)Experiential learningExperiential knowledgeLegal processLawQuality (philosophy)Qualitative researchCriminologyPublic relationsPolitical scienceBusinessSocial scienceEpistemologyComputer science

Abstract

fetched live from OpenAlex

■ Although the civil restraining order is the most commonly sought legal initiative to combat intimate partner violence in British Columbia (BC), no known qualitative research has assessed the application process, and previous quantitative research presents mixed findings. Using interviews, observations, and textual analyses, this institutional ethnography critically analyzes the civil restraining order application process in the BC Provincial Court. Particular attention is paid to disjunctures between abused women's experiential knowledge and what becomes formally known to practitioners who manage their cases. Findings unveil that abused women's lived experience with violence is transformed and shaped into accounts in which their safety needs disappear. Court practitioners become immersed in textually mediated activity within a legal ruling apparatus that emphasizes timely completion of a large quantity of cases, with little or no commitment to quality solutions.

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)0.012
metaresearch head score (Gemma)0.034
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 score0.152
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.009
Scholarly communication0.0070.002
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.027
GPT teacher head0.367
Teacher spread0.340 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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