COVID-19 in Latin America: A High Toll on Lives and Livelihoods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Latin America was hit hard by Covid-19, both in terms of lives and livelihoods. Early lockdowns in the second quarter of 2020 prevented an explosion of deaths at the time but did not stop the pandemic from later wreaking havoc in the region. This paper investigates the dynamics of pandemics in Latin America and how it differed from elsewhere. We probe the role of non-pharmaceutical interventions; the effectiveness (or lack of thereof) lock-downs in Latin America; which structural factors contributed to the high death toll in Latin America, and the extent to which the epidemic harmed the economy. Finally, we briefly analyze the roots of the second-waves that started in the fourth quarter of 2020.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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