Bibliographic record
Abstract
Infectious disease pandemics raise significant and novel ethical challenges to the organization and practice of public health. This chapter provides an overview of the salient ethical issues involved in preparing for and responding to pandemic disease, including those arising from deploying restrictive public health measures to contain and curb the spread of disease (e.g., isolation and quarantine), setting priorities for the allocation of scarce resources, health care workers’ duty to care in the face of heightened risk of infection, conducting research during pandemics, and the global governance of preventing and responding to pandemic disease. It also outlines ethical guidance from prominent ethical frameworks that have been developed to address these ethical issues and concludes by discussing some pressing challenges that must be addressed if ethical reflection is to make a meaningful difference in pandemic preparedness and response.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".