Influenza-Associated Mortality during the 1918–1919 Influenza Pandemic in Alaska and Labrador
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
Some of the most severely affected communities in the world during the 1918–19 influenza pandemic were in Labrador and Alaska. Although these two regions are on the opposite ends of North America, a cultural continuum in the Inuit populations extends throughout the North American Arctic. Both regions contain other population groups, however, and because of these similarities and differences, a comparison of their experiences during the pandemic provides new insights into how culture and environment may influence patterns of spread of infectious disease. We describe here analyses of the patterns of influenza mortality in 97 Alaska communities and 37 Labrador communities. The Alaska communities are divided into five geographic regions corresponding to recognized cultural groups in the region; the Labrador communities are separated into three regions that vary in the degree of admixture between European and indigenous (primarily Inuit) groups. In both Alaska and Labrador mortality was substantially higher than the worldwide average of 2.5–5 percent. Average mortality ranged from less than 1 percent to 38 percent at the regional level in Alaska and from 1 percent to 75 percent at the regional level in Labrador with up to 90 percent mortality in some local communities in both Alaska and Labrador. A number of factors influencing this heterogeneous experience are discussed, including the impact of weather and geography; attempts to protect communities by implementing quarantine policies; accessibility of health care; nutritional deficiencies; cultural factors, such as settlement patterns, seasonal activities, and ethnicity; and exposure to earlier outbreaks of influenza or other diseases that may have increased or lessened the impact of influenza in 1918–19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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