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Record W4413279664 · doi:10.7202/1118801ar

Academic Staff Associations and Vaccination Mandates in the Canadian University Sector

2024· article· en· W4413279664 on OpenAlexaffvenueabout
Alison Braley-Rattai

Bibliographic record

VenueRelations industrielles · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsBrock University
Fundersnot available
KeywordsVaccinationPolitical scienceMedical educationMedicineFamily medicineVirology

Abstract

fetched live from OpenAlex

No COVID-19 mitigation measure has proven more controversial than vaccination mandates. While the prospect of widespread vaccination in 2021 offered hope for a return to pre-pandemic normalcy on Canadian university campuses, proposals for on-campus vaccination mandates sparked intense debate and led to varying approaches at universities. We examine the pivotal role of academic staff associations (ASAs) in advocating and influencing the adoption of vaccination mandates at Canadian universities in the run-up to the fall 2021 term. Through document analysis and semi-structured interviews with ASA leaders and staff, we delve into the factors behind ASA positions on such mandates. Although the vast majority of ASAs advocated robust COVID-19 mitigation measures, including vaccination mandates, their approaches varied because of regional differences and institutional and sectoral dynamics. Many ASAs actively promoted mandatory vaccination, unlike the case with the vast majority of other unions. We argue that ASAs played a crucial role in shaping university responses to the pandemic. Their advocacy contributed to widespread adoption of vaccination mandates before the fall 2021 term. We stress the importance of understanding the nuanced responses of ASAs to such mandates, while shedding light on the interplay of factors behind ASA positions. Abstract We examine the pivotal role of academic staff associations (ASAs) in advocating and influencing the adoption of vaccination mandates at Canadian universities in the run-up to the fall 2021 term. Through document analysis and semi-structured interviews with ASA leaders and staff, we delve into the factors behind ASA positions on such mandates. We demonstrate that the vast majority of ASAs advocated robust COVID-19 mitigation measures, including vaccination mandates, but their approaches varied because of regional differences and institutional and sectoral dynamics. Many ASAs actively promoted mandatory vaccination, unlike the case with the vast majority of other unions.

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)0.014
metaresearch head score (Gemma)0.029
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 score0.918
Threshold uncertainty score0.593

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0240.009
Scholarly communication0.0060.002
Open science0.0010.005
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 head0.029
GPT teacher head0.292
Teacher spread0.263 · 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
Published2024
Admission routes3
Has abstractyes

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