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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".