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Record W4311656669 · doi:10.52975/llt.2022v90.002

Worker Participation in a Time of COVID

2022· article· en· W4311656669 on OpenAlexaffvenueabout
Allan Hall, Eric Tucker

Bibliographic record

VenueLabour / Le Travail · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsYork UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsGovernment (linguistics)Promotion (chess)Public relationsOccupational safety and healthPoliticsBusinessWork (physics)Health carePublic administrationPolitical scienceEconomic growthLawEconomics

Abstract

fetched live from OpenAlex

This study examines worker voice in the development and implementation of safety plans or protocols for covid-19 prevention among hospital workers, long-term care workers, and education workers in the Canadian province of Ontario. Although Ontario occupational health and safety law and official public health policy appear to recognize the need for active consultation with workers and labour unions, there were limited – and in some cases no – efforts by employers to meaningfully involve workers, worker representatives (reps), or union officials in assessing covid-19 risks and planning protection and prevention measures. The political and legal efforts of workers and unions to assert their right to participate and the outcomes of those efforts are also documented through archival evidence and interviews with worker reps and union officials. The article concludes with an assessment of weaknesses in the government promotion and protection of worker health and safety rights and calls for greater labour attention to the critical importance of worker health and safety representation.

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)0.009
metaresearch head score (Gemma)0.021
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.674

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0180.007
Scholarly communication0.0060.002
Open science0.0020.011
Research integrity0.0020.003
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 head0.066
GPT teacher head0.437
Teacher spread0.371 · 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

Citations4
Published2022
Admission routes3
Has abstractyes

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