Covid-19 in Asia: Governance and the Politics of the Pandemic
Bibliographic record
Abstract
In this introduction to studies of the politics of the COVID-19 pandemic in four Asian states—India, Pakistan, Vietnam, and South Korea—we first discuss the difficulties in evaluating the performances of different countries, given the varying reliability of data and the different possible criteria that may be applied. In our studies we aim rather to illuminate the process of different state responses, and we go on to summarize evidence on different patterns of response across Asia, situating the four country studies in a comparative context. We then review arguments in the literature about the determinants of different responses, before presenting our framework for the analysis of the politics that underlie these differences. Political leadership has undoubtedly exercised a powerful influence, but in the structural context of the relationships of state and citizens. We argue that understanding of these relationships is advanced by an analytical framework that draws on state-in-society approaches developed in the work of Joel Migdal, Michael Mann, and Peter Evans.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| 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 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".