<i>Managing Federalism through Pandemic</i>, by Kathy L. Brock and Geoffrey Hale
Bibliographic record
Abstract
How might a decentralized federation address a series of evolving interconnected crises? What implications can this have for federalism itself? Managing Federalism through Pandemic provides a holistic analysis of Canadian federalism in the wake of the most important public health crisis to affect countries around the world in the twenty-first century. The volume, edited by Kathy L. Brock and Geoffrey Hale, assesses Canada’s coronavirus disease 2019 (COVID-19) responses throughout the first 18 months of the pandemic. This book, however, is more than a rereading of policymaking during the pandemic, as the authors use the COVID-19 pandemic as a foil for a deeper discussion on the appropriate balance of centralization and decentralization in the Canadian federation. Managing Federalism through Pandemic therefore contributes to federalism scholarship by placing Canada’s institutional context at the forefront of empirical analyses and suggesting paths forward through detailed policy prescriptions. Comprising 14 chapters authored by academics and practitioners from the fields of political science, public policy, and economics, Managing Federalism through Pandemic includes a diverse array of contributors who credibly cover a wide range of topics including public health, emergency management, fiscal federalism, national security, indigenous rights, as well as energy, social, and economic policies. The book is divided into sections which cover governmental efforts to anticipate and manage the pandemic itself, interventions into social and economic issues arising from the pandemic, as well as lessons for the federation from the pandemic. Each chapter offers a well-researched account of the subject matter and provides an explanation of key actions taken during the period under consideration. Most authors also discuss policy recommendations and opportunities for learning and incorporate normative elements to provide insights into future challenges facing Canada. In the concluding chapter, the editors bring together these diverse perspectives by returning to the proposed analytical framework, which focuses on the extent to which governments operate alone (self rule) or cooperate with another level of government (shared rule). Using a continuum of intergovernmental cooperation, in the conclusion, Brock and Hale map each field covered by the volume.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.021 |
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".