“Responding to an Epidemic Requires a Compassionate State”: How Has the Indian State Been Doing in the Time of COVID-19?
Bibliographic record
Abstract
In response to the COVID-19 pandemic, the Indian government, led by Narendra Modi, imposed a stringent lockdown with only four hours notice. It paid no attention to the millions of migrants who work on a temporary basis in Indian cities. Most lost their livelihoods as a result of the lockdown, and millions sought to return to their native villages. At the same time, the rural economy confronted its own difficulties caused by the lockdown. The relief that the Modi government offered to the large numbers of poor people who had been adversely affected by its response to COVID-19 was limited and poorly delivered. The episode showed the lack of responsiveness of Indian democracy to the needs of working people and the failures of development. Yet Modi's particular brand of authoritarian populism worked so well that a government displaying very little compassion retained strong popular support.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".