Why Was the Pandemic Poorly Managed by the Government of India? a State-in-Society Approach
Bibliographic record
Abstract
Administrative "success" or "failure" during the pandemic are hard to assess given uncertainties both of criteria and of data. But there can be no doubt about the mishandling of the pandemic at crucial junctures by the Indian government, or about the culpability of prime minister Narendra Modi himself. He has this in common with other "strongmen" of contemporary world politics, but Modi was unusually successful in turning the events of the pandemic to reinforce his dominance. The immediate political factors that influenced the Indian response had to do with political leadership and with the "decisionism" that characterised Modi's actions, but in the context of the pursuit of the goals of Hindu nationalism. This article explains the responses of the Indian government drawing on a framework based on the comparative analysis of Baum and her co-authors. It shows how the events of the pandemic reflect on India's politics and on the character of the Indian state, using a state-in-society approach suggested by the interlocking arguments of Migdal, Mann and Evans. This highlights and explains the very different responses of the major states of the country.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".