Introduction: Politics, Public Opinion and the COVID-19 Pandemic in Latin America
Bibliographic record
Abstract
In early 2020, Covid-19 spread from China, first to countries such as Italy and Iran, then across the globe, causing high death tolls and the shutdown of socie-ties (CDC, 2021). As the pandemic moved to Latin America, the region’s leaders responded to the threat in wide-ranging ways. Governments deployed a variety of public health and economic measures to stem the human and financial costs of the pandemic. Some minimized the danger: from President Bolsonaro’s widely criti-cized labeling of the disease as a “little flu” in Brazil (Friedman, 2020), to President Andres Manuel Lopez Obrador’s (AMLO’s) refusal to wear masks in Mexico (Mo-rales, 2021). Others responded forcefully, such as Fernandez’s swift lockdown in Argentina (Reuters, 2020). While some drew heavily on progressive social move-ments in the policymaking process, others sidelined and marginalized them (Abers et al. 2021). The efficacy of these responses also varied widely. Some countries, such as the Dominican Republic, experienced Covid death rates that were lower than wealthy countries such as Canada and Denmark; others saw immense loss of life, as in Peru, where 600 people have died for every 100,000. Despite this vari-ation, the region on average has suffered terribly during the pandemic, with many countries in the top twenty for both cases and loss of life per capita (Blofield et al.2020; Fernandez and Machado, 2021; Ritchie et al. 2020).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".