Beyond COVID‐19: Five commentaries on expert knowledge, executive action, and accountability in governance and public administration
Bibliographic record
Abstract
Abstract Several Canadian and international scholars offer commentaries on the implications of the COVID‐19 pandemic for governments and public service institutions, and fruitful directions for public administration research and practice. This first suite of commentaries focuses on the executive branch, variously considering: the challenge for governments to balance demands for accountability and learning while rethinking policy mixes as social solidarity and expert knowledge increasingly get challenged; how the policy‐advisory systems of Australia, Canada, New Zealand, and United Kingdom were structured and performed in response to the COVID‐19 crisis; whether there are better ways to suspend the accountability repertoires of Parliamentary systems than the multiparty agreement struck by the minority Liberal government with several opposition parties; comparing the Canadian government’s response to the COVID‐19 pandemic and the Global Financial Crisis and how each has brought the challenge of inequality to the fore; and whether the COVID‐19 pandemic has accelerated or disrupted digital government initiatives, reinforced traditional public administration values or more open government.
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 | 0.089 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.043 | 0.037 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.049 | 0.064 |
| Insufficient payload (model declined to judge) | 0.009 | 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".