The Expert and the Lay Public: Reflections on Influenza A (H1N1) and the Risk Society
Bibliographic record
Abstract
Trust between the lay public and scientific experts is a key element to ensuring the efficient implementation of emergency public health measures. In modern risk societies, the management and elimination of risk have become preeminent drivers of public policy. In this context, the protection of public trust is a complex task. Those actors involved in public health decision-making and implementation (e.g., mass vaccination for influenza A virus) are confronted with growing pressures and responsibility to act. However, they also need to accept the limits of their own expertise and recognize the ability of lay publics to understand and be responsible for public health. Such a shared responsibility for risk management, if grounded in participative public debates, can arguably strengthen public trust in public health authorities and interventions.
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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 | 0.065 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.068 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.034 | 0.041 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".