Towards a Systematic Understanding of How to Institutionally Design Scientific Advisory Committees
Bibliographic record
Abstract
Scientifically-derived insights are often held as requirements for defensible policy choices. Scientific advisory committees (SACs) figure prominently in this landscape, often with the promise of bringing scientific evidence to decision-makers. Yet, there is sparse and scattered knowledge about what institutional features influence the operations and effectiveness of SACs, how these design choices influence subsequent decision-making, and the lessons learned from their application. The consequences of these knowledge gaps are that SACs may not be functioning as effectively as possible. The articles in this special journal issue of Global Challenges bring together insights from experts across several disciplines, all of whom are committed to improving SACs’ effectiveness worldwide. The aim of the special issue is to inform future SAC design in order to help maximize the application of high-quality scientific research for the decisions of policymakers, practitioners, and the public alike. In addition to providing an overview of the special issue and a summary of each article within it, this introductory essay presents a definition of SACs and a conceptual framework for how different institutional features and contextual factors affect three proximal determinants of SACs’ effectiveness, namely the quality of advice offered, the relevance of that advice, and its legitimacy.
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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 | 0.539 | 0.563 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.019 | 0.044 |
| Scholarly communication | 0.050 | 0.046 |
| Open science | 0.011 | 0.021 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".