An Overview of Systematic Reviews to Inform the Institutional Design of Scientific Advisory Committees
Bibliographic record
Abstract
The current lack of synthesized evidence for informing the design of scientific advisory committees (SACs) is surprising in light of the widespread use of SACs throughout decision-making processes. While existing research points to the importance of quality, relevance, and legitimacy for SACs' effectiveness, those planning SACs would benefit from efforts to systematically pinpoint optimal designs of these committees for maximal effectiveness. Search strategies are developed for seven electronic databases. Of the 1895 systematic reviews identified, six reviews meet the inclusion criteria: they report the results of systematic reviews that followed a clearly identified systematic methodology, examine factors related to the design of SACs, and involve processes in the natural or social sciences. These reviews collectively summarize 444 primary studies. Three of the six reviews look at the impacts of SAC size, two evaluate the influence of the committee's diversity, and half mention the importance of properly on-boarding new members. The goal is to identify recurring themes to understand the specific institutional features that optimize the usefulness of SACs. In turn, this overview of systematic reviews aims to contribute to a growing body of literature on how SACs should be designed to maximize their effectiveness and helpfulness for decision-making.
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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 | 0.100 | 0.264 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.074 | 0.050 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".