Learning from Latin America: Coordinating Policy Responses across National and Subnational Levels to Combat COVID-19
Bibliographic record
Abstract
We provide policy lessons for governments across Latin America by drawing on an original dataset of daily national and subnational non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic for eight Latin American countries: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, and Peru. Our analysis offers lessons for health system decision-making at various levels of government and highlights the impact of subnational policy implementation for responding to health crises. However, subnational responses cannot replace coordinated national policy; governments should emphasize the vertical integration of evidence-based policy from national to local levels while tailoring local policies to local conditions as they evolve. Horizontal policy integration across sectors and jurisdictions will also improve coordination at each level of government. The Latin American experiences with policy and politics during the COVID-19 pandemic project glocal health policy recommendations that connect global considerations with local needs.
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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 | 0.002 | 0.238 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".