Academic Staff Associations and Vaccination Mandates in the Canadian University Sector
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".