Policy responses to COVID-19: A reassessment
Bibliographic record
Abstract
Description: Universities and colleges (hereafter “postsecondary institutions”) across the world have adopted a broad range of policy responses to manage the COVID-19 crisis. While national, provincial, and local public health regulations have set the frame to formulate, develop, and implement these responses, these have differed among postsecondary institutions within the same countries and even among localities. However, the how, who, and why of a decision-making process that led to similarities and differences in COVID-19 policies, especially concerning medical mandates, has not been documented or appraised, even when the policy decisions have dramatically, and differentially, affected the lives of millions in the academy and beyond – students, staff, faculty, and their families. We will conduct a scoping review of the documentary evidence on the decision-making process concerning COVID-19 policy in Canadian postsecondary institutions, with an emphasis on decisions concerning medical mandates. Following Arksey and O’Malley’s seminal framework, enhanced by the team-based approach of Levac and colleagues, we aim to describe, summarize, and disseminate the evidence on our object of study, as well as identify gaps in the literature that can inform the direction of future research and policy. Review questions: 1. What is known from the existing literature about the decision-making process in Canadian postsecondary institutions concerning COVID-19 policy? 2. What can be gleaned from other sources of evidence – grey literature and university messaging – about the dynamics of the decision-making process in Canadian postsecondary institutions concerning COVID-19 policy, especially medical mandates?
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 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 | 0.066 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".