Framing problems, governing practices: A critical scoping review of COVID-19 policy in Canadian post-secondary education
Bibliographic record
Abstract
When the World Health Organization declared COVID-19 a pandemic, unprecedented policy responses ensued in higher education, with Canadian post-secondary institutions (PSIs) rapidly adopting radical measures, including campus closures, masking requirements, and vaccine mandates. These policies were widely justified as evidence-based, ethical, and legal. This critical scoping review examined the COVID-19 policy responses at five Ontario PSIs. Using Carol Bacchi’s What is the Problem Represented to Be? approach, we explored how problems were framed, decisions made, and ethical principles invoked. Data included publicly available policy documents, union statements, and legal decisions. PSIs represented the problem as one of a deadly, “equal opportunity” virus, demanding maximum compliance with public health directives, particularly vaccination. This framing dominated governance practices, often sidelining collegial decision-making in favor of executive authority and ad hoc committees. Claims of a scientific consensus were central to policy justification, despite initial and growing evidence—such as low infection fatality rates among young adults, the strength of natural immunity, the failure of vaccines to stop transmission, and reports of vaccine-related adverse events—challenging that framing. Equity, diversity, and inclusivity were frequently invoked to support these policies, yet the same measures often excluded individuals with diverse needs and applied exemptions inconsistently. The COVID-19 response in Canadian PSIs reflected not a true consensus but an illusion of consensus, produced through the foreclosure of debate and suppression of dissent—patterns at odds with the normative values of higher education.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: yes · About a Canadian topic: yes | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: yes | Systematic review | low |
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 | 0.170 | 0.349 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.055 | 0.067 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".