How experts are chosen to inform public policy: Can the process be improved?
Bibliographic record
Abstract
The ever-increasing complexity of the food supply has magnified the importance of ongoing research into nutrition and food safety issues that have significant impact on public health. At the same time, ethical questions have been raised regarding conflict of interest, making it more challenging to form the expert panels that advise government agencies and public health officials in formulating nutrition and food safety policy. Primarily due to the growing complexity of the interactions among government, industry, and academic research institutions, increasingly stringent conflict-of-interest policies may have the effect of barring the most experienced and knowledgeable nutrition and food scientists from contributing their expertise on the panels informing public policy. This paper explores the issue in some depth, proposing a set of principles for determining considerations for service on expert advisory committees. Although the issues around scientific policy counsel and the selection of advisory panels clearly have global applicability, the context for their development had a US and Canadian focus in this work. The authors also call for a broader discussion in all sectors of the research community as to whether and how the process of empaneling food science and nutrition experts might be improved.
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.
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 | 0.708 | 0.719 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.045 | 0.051 |
| Scholarly communication | 0.062 | 0.062 |
| Open science | 0.018 | 0.034 |
| Research integrity | 0.077 | 0.067 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".