A Typology of Scientific Advisory Committees
Bibliographic record
Abstract
The era of evidence-informed decision-making has seen increased use of the scientific advisory committee (SAC) to provide decision-makers with scientific advice, despite limited evidence of the effectiveness or best strategies for designing these committees. In this study, an in-depth review of academic and gray literature is undertaken to outline the global landscape of SACs. The development of a typology is also undertaken that categorizes SACs along six dimensions: 1) sector, 2) level of operation, 3) permanence, 4) target audience, 5) autonomy, and 6) nature of advice. It is found that SACs differ profoundly in each of these dimensions and provide examples demonstrating this variation. The landscape and typology can help decision-makers understand the key elements of SAC design and reform, and the results will also inform future research on the design and effectiveness of SACs. With SACs expected to promote evidence-informed decision-making, it is imperative that the design of these committees themselves is guided by evidence.
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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 | 0.057 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.028 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".