“It Depends on Where You Look”: The Unusual Presentation of Scurvy and Smallpox Among Klondike Gold Rushers as Revealed Through Qualitative Data Sources
Bibliographic record
Abstract
Health in the context of frontier boomtown communities represents an underdeveloped topic of research both within the social sciences and beyond. Studies of such historic communities offer insight into the human condition in past populations. They provide valuable observations with far-reaching modern-day applications, as many of the issues faced by the Klondike Gold Rushers are similarly experienced by those residing in single-industry and resource communities experiencing fast change in the remote wilderness. These communities present a unique biosocial context for the experience of disease and disorders, as is evident in the case of both scurvy and smallpox when they erupted in the Klondike gold fields. Yet, for various reasons, these diseases remained invisible when quantitative data sources only were used. The important implications that these sicknesses held for the health status of the gold rushers would thus have been undetected had analysis focused solely upon the customary morbidity and mortality data sources, resulting in a distorted view of the human condition in the context of this celebrated event in Canadian history. Only when qualitative materials are also explored does the full picture of the health in this historic population come into focus, while also revealing much more about life in this particular time and place than simply what illnesses the Klondikers suffered and died from.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".