Beyond COVID‐19: Five commentaries on reimagining governance for future crises and resilience
Bibliographic record
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 second suite of commentaries considers the challenges confronting governments as a result of the COVID-19 pandemic and in the decades to come with an increasingly broad lens: the need to understand and rethink the architecture of the state given recent and future challenges awaiting governments; the need to rethink government-civil society relations and policies to deliver services for increasingly diverse citizens and communities; the need for new repertoires and sensibilities on the part of governments for recognizing, anticipating, and engaging on governance risks despite imperfect expert knowledge and public skepticism; how the COVID-19 crisis has caused us to reconceive international and sub-national borders where new "borders" are being drawn; and the need to anticipate a steady stream of crises similar to the COVID-19 pandemic arising from climate change and related challenges, and develop new national and international governance strategies for fostering population and community resilience.
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 | 0.041 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.038 | 0.039 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.053 | 0.068 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".