Mobilizing Policy (In)Capacity to Fight COVID-19: Understanding Variations in State Responses
Why this work is in the frame
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Bibliographic record
Abstract
The objective of this collection of essays is to gain insights into the different national-level state responses to COVID-19 around the world and the conditions that shaped them. The pandemic offers a natural experiment wherein the policy problem governments faced was the same but the responses they made were different, creating opportunities for comparison of both the kinds of policy tools being used and the factors that accounted for their choice. Accordingly, after surveying on-line databases of policy tools used in the pandemic and subjecting these to topic modelling to reveal the characteristics of a 'standard' national pandemic response, we discuss the similarities and differences found in specific responses. This is done with reference to the nature and level of policy capacity of respective governments, highlighting the critical roles played by (in)adequate preparation and lesson-drawing from past experiences with similar outbreaks or crises. Taken together the articles show how the national responses to the COVID-19 pandemic were shaped by the opportunity and capacity each government had to learn from previous pandemics and their capacity to operationalize and build political support for the standard portfolio of policy measures deployed to deal with the crisis. However, they also show how other factors such as the nature of national leadership, the organization of government and civil society, and blindspots towards the vulnerabilities of certain population segments also helped to shape policy responses to the pandemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it