Appraising the decision-making process concerning COVID-19 policy in postsecondary education in Canada: A critical scoping review protocol
Bibliographic record
Abstract
Background: Responses to COVID-19 in Canadian postsecondary education have overhauled usual norms and practices, with policies of unclear rationale implemented under the pressure of a reported public health emergency. Objective: To critically appraise the decision-making process informing COVID-19 policy in the postsecondary education sector. Methods: Our scoping review will draw from macro and micro theories of public policy, specifically the critical tradition exemplified by Carol Bacchi's approach "What is the problem represented to be" and will be guided by Arksey and O'Malley's framework for scoping reviews and the team-based approach of Levan and colleagues. Data will include diverse and publicly available documents to capture multiple stakeholders' perspectives on the phenomenon of interest and will be retrieved from university newsletters and legal websites using combinations of search terms adapted to specific data types. Two reviewers will independently screen, chart, analyze and synthesize the data. Disagreements will be resolved through full team discussion. Discussion: Despite the unprecedented nature of the mass medical mandates implemented in the postsecondary sector and their dramatic impact on millions of lives-students, faculty, staff and their families, friends and communities-the decision-making process leading to them has not been documented or appraised. By identifying, summarizing and appraising the evidence, our review should inform practices that can contribute to effective and equitable public health policies in postsecondary institutions moving forward.
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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 | 0.407 | 0.427 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.034 | 0.030 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".