Preparing for Another Round of Swine Flu: Will the WHO's Plan Prove to be a Success for the Global Community and Will the U.S. Lead the Way?
Bibliographic record
Abstract
Abstract This article seeks to determine whether countries in the global community have governance systems or domestic laws that will enable them to be effective in their preparation and response to influenza pandemics. It begins by analyzing the 2009 H1N1 pandemic, discussing the events leading up to Mexico triggering the IHRs and determining which aspects of Mexico’s pandemic influenza preparedness and response should be used again in the future and which should be retired. It will then take a closer look at how six WHO member states—Australia, Canada, France, Japan, the United Kingdom, and the United States—have or have not integrated lessons learned from the swine flu into their own plans for outbreaks of influenza, evaluating the plans from a comparative perspective in order to determine which plans provide the best guidance for the future.
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How this classification was reachedexpand
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 | 0.006 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".