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Record W3021857073

Safe at work: Options for British Columbia to support survivors of domestic violence in the workplace

2020· article· en· W3021857073 on OpenAlexaboutno aff
Lúcia Hisako Takase Gonçalves

Bibliographic record

VenueSummit (Simon Fraser University) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Occupational safety and healthWorkplace violenceSuicide preventionPoison controlPsychologyCriminologyPolitical scienceMedicineMedical emergencyEngineeringLaw
DOInot available

Abstract

fetched live from OpenAlex

As one of the last Canadian provinces to implement domestic violence leave, British Columbia lags behind pan-Canadian standards on support for survivors of domestic violence (DV) in the workplace.Studies have demonstrated that domestic violence (DV) experienced in the personal life of an employee can produce negative externalities in the workplace for survivors, co-workers, perpetrators, and employers.Using a literature review, jurisdictional scan, and expert interviews, this study helps to fill the gap in the literature by examining what changes need to occur in British Columbia to better support survivors of domestic violence in the workplace.The options evaluated include a review of the status quo, occupational health and safety regulations, and a province-wide women's advocate program.The study concludes with the recommendation for BC to amend occupational health and safety regulations to incorporate both the psychological and physical aspects of DV as a workplace hazard.

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)0.002
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.001
Scholarly communication0.0040.002
Open science0.0030.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.024
GPT teacher head0.265
Teacher spread0.241 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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