Pandemic failure, democratic backslide: Why India’s autocratic turn under Prime Minister Narendra Modi matters to Canada and the world
Bibliographic record
Abstract
The desperate search for scarce oxygen supplies, frontline workers on the verge of breakdown, funeral pyres burning through the night: the second wave of the pandemic in India is a genuine humanitarian catastrophe. Officially, the daily number of cases and deaths exceeded 300,000 and 4,000 at their peak this spring. Independent epidemiological studies suggest the toll might be even worse, between 8000 and 32,000 excess deaths a day, according to reports in the Economist. Yet it was only in January that daily mortality rates officially fell to less than 200 a day, leading Prime Minister Narendra Modi to declare at the World Economic Forum: India “has saved humanity from a big disaster by containing Corona effectively.” New Delhi proceeded to launch a national vaccination drive, setting a target of 250 million by July, a bold figure in absolute terms. More strikingly, the Modi government decided to distribute vaccines freely to its neighbors in the subcontinent, and then to many low-income countries far beyond. A desire to match China’s vaccine diplomacy, and India’s impressive production capacity, motivated and enabled its largesse. The move stoked national pride and cast rich western democracies, which were hoarding limited vaccine supplies for themselves, in a terrible light. Then a disaster unfolded. How did it go so wrong?
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.022 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".