Beyond Biology: Understanding the Social Impact of Infectious Disease within Two Aboriginal Communities in Manitoba
Bibliographic record
Abstract
Health crises such as the SARS epidemic and H1N1 have rekindled interest in the 1918 influenza pandemic, which swept the globe in the wake of the First World War and killed approximately 50 million people. Now more than ever, medical, public health, and government officials are looking to the past to help prepare for future emergencies.\nEpidemic Encounters zeroes in on Canada, where one-third of the population took ill and fifty-five thousand people died, to consider the various ways in which this country was affected by the pandemic. How did military and medical authorities, health care workers, and ordinary citizens respond? What role did social inequalities play in determining who survived? To answer these questions as they pertained to both local and national contexts, the contributors explore a number of key themes and topics, including the experiences of nurses and Aboriginal peoples, public letter writing in Montreal, the place of the epidemic within industrial modernity, and the relationship between mourning and interwar spiritualism.\nThe Canadian experience brings to light the complex ways that biology, science, society, and culture intersect in a globalizing world and offers new insight into medical history’s usefulness in the struggle against epidemic disease.\nThis book will be of value not only to historians and medical anthropologists but also to clinicians and government officials charged with planning responses to pandemic diseases.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.055 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".