Network dynamics in public health advisory systems: A comparative analysis of scientific advice for COVID‐19 in Belgium, Quebec, Sweden, and Switzerland
Bibliographic record
Abstract
Abstract This study presents a dual‐method approach to systematically analyze public health advisory networks during the COVID‐19 pandemic across four jurisdictions: Belgium, Quebec, Sweden, and Switzerland. Using network analysis inspired by egocentric analysis and a subsystems approach adapted to public health, the research investigates network structures and their openness to new actors and ideas. The findings reveal significant variations in network configurations, with differences in density, centralization, and the role of central actors. The study also uncovers a relation between network openness and its structural attributes, highlighting the impact of network composition on the flow and control of expert advice. These insights into public health advisory networks contribute to understanding the interface between scientific advice and policymaking, emphasizing the importance of network characteristics in shaping the influence of expert advisors. The article underscores the relevance of systematic network descriptions in public policy, offering reflections on expert accountability, information diversity, and the broader implications for democratic governance.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".