Governing pandemics: Resilience and community responses for COVID-19 in Bengaluru and Shanghai
Bibliographic record
Abstract
This study explores governance strategies and community responses to the COVID-19 pandemic in Bengaluru and Shanghai. It builds on recent evidence showing China centers on territorial institutions to respond to the pandemic, whereas democratic India relies on associational politics, including alliances with different stakeholders. The study argues that increased community involvement in Shanghai arose from the state’s inadequacies during the crisis, while resident welfare associations (RWAs) in Bengaluru primarily served the middle class and had limited impact on vulnerable populations. Using a mixed-methods approach, the study highlights key lessons that contributed to effective responses in both cities, offering policy recommendations for building resilient cities with stronger leadership. Community reactions in India and China surpassed the normal during the pandemic. Despite differences in urban regimes and political systems, Shanghai’s territorial institutions contrasted with Bengaluru’s associational approach; however local governance and community efforts significantly shaped their pandemic outcomes during the health disaster.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".