Early COVID-19 policy responses in Latin America: a comparative analysis of social protection and health policy
Bibliographic record
Abstract
In May 2020, the World Health Organization (WHO) declared Latin America an epicenter of the global COVID-19 pandemic. Governments have taken different approaches to tackling the crisis, but it is not clear if policies to address the health and economic dimensions of COVID-19 represent a radical departure from business as usual, or whether they simply reflect and reproduce unequal power relations and flawed institutions. This article seeks to address these questions by examining the health and social protection measures Latin American governments implemented in response to COVID-19 in the early stages of the pandemic. We argue that, while there is cross-country variation with respect to COVID-19 policy, the similarities are more striking than the differences. Presidents play a decisive role in the policymaking process, particularly during a time of crisis, and their preferences explain some of the variation we see. But we find that decision-makers and bureaucracies are influenced and constrained by their countries’ institutions. Even during a global pandemic, policy reflects path dependency and serves to protect established interests while neglecting the needs of marginalized populations.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".