Governing a Pandemic: Assessing the Role of Collaboration on Latin American Responses to the COVID-19 Crisis
Why this work is in the frame
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Bibliographic record
Abstract
How do governments address complex, cross-sectoral problems, like the COVID-19 pandemic? Why were some Latin American countries more successful at containing the pandemic's most devastating health outcomes? We argue that national governments that were more collaborative in their response to COVID-19 were more successful in reducing death rates. Our original dataset offers a novel attempt to operationalise collaborative governance (CG). We undertake simple statistical tests to measure the relationship between CG and COVID-19-related mortality rates in Latin America. We then choose three case studies to assess whether collaboration was meaningful in practice. Initial evidence suggests governments that pursued CG were more effective at containing mortality rates early on in the pandemic. The collaboration helped to foster cooperation over resources; buy time to prepare for a potential case surge; and produce a unified message regarding what citizens should do to prevent viral spread.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it