A Typology of Scientific Advisory Committees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.034 | 0.120 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it