A review of expert group-based science advisory processes in Canada
Bibliographic record
Abstract
Some of the most authoritative science advice comes from groups of experts operating under systematic advisory methods. In this paper, we compiled publicly available information on 676 science advisory processes conducted over a 29-year period by five Canadian science advisory institutions (i.e., the Canadian Science Advisory Secretariat, the National Advisory Council on Immunization, the Council of Canadian Academies, the Royal Society of Canada, and the Committee on the Status of Endangered Wildlife in Canada) and used these data to explore how these institutions operate. Despite common objectives, we found considerable variation among institutions, including in the number of experts involved in developing and reviewing advice, the length of resulting science advice documents, and the time required for individual science advisory processes to be completed. In general, the institutions have become more transparent over time, driven primarily by disclosing more information on the experts involved. Additionally, we found that science advice reports have become lengthier, and the delivery of science advice now takes considerably more time for most institutions. We discuss these findings in the context of recent criticisms of expert group-based science advisory processes and suggest there may be trade-offs associated with emphasizing different science advice principles.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".