Explaining variations in state COVID-19 responses: psychological, institutional, and strategic factors in governance and public policy-making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The fight to curb the spread of COVID-19 underscores the central role that governments play in many policy areas, including public health, and the need to understand the reasons for observed differences in governance responses to the pandemic in different countries and jurisdictions. Drawing on secondary sources and media interviews with prime minister and ministers, the paper demonstrates how examination of a combination of psychological, institutional, and strategic factors operating in policy and governance arenas helps explain the policy and governance choices different governments made in their fight against COVID-19. Psychological factors include elite panic and limited government attention spans while institutional factors include the level of government effectiveness, their degree of freedom to manouevre, levels of social trust, the existence of separate ministries of health and health ministers with a medical background, the extent to which ruling parties are well established, state governors’ actual power vis-à-vis the federal government, as well as a legacy of generous social policy and existent universalistic social programs. Strategic factors include political considerations underlying policy and governance choices when elected executives face deep uncertainty. Focusing on these factors and arenas helps state-centric governance theory produce explanations rather than describe patterns of policy-making.
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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 | 0.001 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it