“Destroying Generation after Generationâ€: Outbreaks of Smallpox in the Cuchumatán Highlands of Guatemala (1780-1810)
Bibliographic record
Abstract
The advent of Covid-19, unforeseen though it was, and destructive though it remains, affords timely opportunity to reflect on the occurrence of past pandemics and their impact on humankind. Devastating as the Black Death in fourteenth-century Europe is known to be, loss-of-life caused too, in the wake of World War I, by the Spanish Flu, both pandemics pale when compared to the mortality of Native Americans following the Columbus landfall. Guatemala and its Indigenous Maya peoples, especially those of the Sierra de los Cuchumatanes, are discussed as a case in point. Demographic collapse here, begun in the 1520s, continued well into the seventeenth century, after which attrition abated and recovery set in – slowly, and not without reversals, as scrutiny of the ravages wrought by the re-occurrence of smallpox between 1780 and 1810 vividly attests. As with the success of vaccines made to combat the scourge of Covid-19, so also did Edward Jenner’s experiments with inoculation prove beneficial, even when they reached and were administered in one of Guatemala’s most isolated and intractable parts. Thereafter, Indigenous numbers stabilized and began to grow, guaranteeing Maya survival.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".