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Record W4404408935 · doi:10.52975/llt.2024v94.005

Union Responses to Workplace COVID-19 Vaccine Mandates in Canada

2024· article· en· W4404408935 on OpenAlexaffvenueabout
Alison Braley-Rattai

Bibliographic record

VenueLabour / Le Travail · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsBrock University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakVirologyPandemicPolitical scienceMedicineOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

This article explores union responses to workplace-based covid-19 vaccine mandates in Canada. Specifically, the authors examine the complex interplay of factors that drove unions to adopt their respective positions on vaccine mandates and to frame those positions in particular ways for the benefit of their members and the wider public. Interviews with key informants, along with analysis of documents and arbitration decisions, reveal a disjuncture between the discursive quality of certain unions’ positions and their actual positions. In particular, media framing of unions as either “for” or “against” vaccine mandates oversimplified or misrepresented the actual positions adopted. In response, the article introduces a typology of union positions that distinguishes between support for mandatory-vaccination policies and support for voluntary-vaccination policies and reveals that the vast majority of unions favoured the latter. The authors further reveal that workplace vaccine mandates were both internally divisive and disorienting for unions, given the central role labour organizations play in managing workplace disputes and representing the interests of workers, both individually and collectively.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.289
Teacher spread0.274 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2024
Admission routes3
Has abstractyes

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