O surto da COVID-19 e as respostas da administração municipal: munificência de recursos, vulnerabilidade social e eficácia de ações públicas
Bibliographic record
Abstract
Abstract Facing the unprecedented situation of the COVID-19 pandemic, public officials at the municipality-level have no clear benchmarks or tested policies. In this situation, decision-making becomes a controversial process. This article provides insights for public agents in the Brazilian municipalities to deal with the initial stages of the COVID-19 pandemic. We analyzed the actions taken by city halls of the 52 Brazilian municipalities at least thirty days since the first confirmed case of COVID-19. We used a fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify the combinations of contextual factors and public actions that reduced COVID-19 transmission during the critical initial stage. The empirical results show three main paths to guide policy-making: (1) a plural collaboration path involving public and private sectors, operating in a fragile health system; (2) a public action path providing aid programs through intense collaboration inside public bureaucracy; and (3) a resource-based path relying on a well-structured health system.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".