Policy‐making, policy‐taking, and policy‐shaping: Local government responses to the COVID‐19 pandemic
Bibliographic record
Abstract
Abstract The COVID‐19 pandemic has challenged nations states across the world. They have implemented lockdown and social distancing and with the development of vaccines have gone to great lengths to build herd immunity for their populations. As place managers, local government has played a variety of roles supporting central government edicts related to social distancing and supporting local businesses impacted by lockdowns. The research reported here comparing the role local government has played in Australia, Canada, Italy, and New Zealand shows that they have at different times and for different issues been policy takers from central government, policy shapers, and policy makers adapting national strategies. Local government plays an important complementary role with central governments in both unitary and federal systems of government. The paper contributes to the literature on multi‐level governance, place‐based decision‐making, and disaster and emergency management by offering a framework for analysing municipal roles in crises management both in their relationship with higher layers of government and in their acting as locally placed organisations. Points for practitioners Cross‐national study: Australia, Canada, Italy, and New Zealand. Examination of local government responses to COVID‐19 pandemic as policy makers, takers, or shapers. Comparison of federal and unitary states.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".