Policy responses to COVID-19: A reassessment
Bibliographic record
Abstract
This project critically appraises selected dimensions of the policy response to the COVID-19 crisis. It is part of a broader collaboration with a group of interdisciplinary researchers investigating the post-pandemic recovery and best practices for future emergencies funded by a $500,000 award from the New Frontiers in Research Fund (NFRF). The broader project is led by Prof. Claus Rinner, from Toronto Metropolitan University, and includes a team of five coinvestigators, in alphabetical order, Prof. Claudia Chaufan, York University, Prof. Candice Chow, McMasters University; J. Christian Rangel, University of Ottawa; Elaine Wiersma, Lakehead University; and Wang, Yiwen, University of Toronto, collaborators from across Canada with expertise in neuroscience, toxicology, law, media and communications, and international collaborators, from Jamaica, Western Europe, Israel, Kenya, and Uganda with expertise in behavioural sciences, economics, epidemiology, and philosophy.
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".