Scientific Certainty in a Time of Uncertainty: Predicting Vulnerability of Canada's First Nations to Pandemic H1N1/09
Bibliographic record
Abstract
On June 11, 2009, the Director General of the World Health Organization, Dr. Margaret Chan, announced that the scientific evidence indicated that the criteria for an influenza pandemic had been met: pandemic H1N1/09 virus, the first in nearly 40 years, was officially upon us. The World Health Organization has estimated that as many as 2 billion or between 15 and 45 percent of the population globally will be infected by the H1N1/09 virus. Scientists and governments have been careful to walk a line between causing mass public fear and ensuring people take the risks seriously. The latest information indicates that the majority of individuals infected with the H1N1/09 virus thus far have suffered mild illness, although very severe and fatal illness have been observed in a small number of cases, even in young and healthy people (World Health Organization 2009c). There is no evidence to date that the virus has mutated to a more virulent or lethal form; however, as we enter the second wave of the pandemic, a significant number of people in countries across the world are susceptible to infection. Most importantly, certain subgroups have been categorized as high risk given the clinical evidence to date. One of these subgroups is Indigenous populations (World Health Organization 2009c).
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".