Reflections on science advisory systems in Canada
Bibliographic record
Abstract
Abstract As the evolution of our world has triggered complexity and technological sophistication, it is now essential to consider sound scientific evidence as an integral element of decision-making. Science advisers or chief scientists have to take into account many factors in giving advice. Depending on the nature and level of advice, factors such as the ideology of the governing body, the state of the social, economic and scientific development in the country or region, potential impacts on the health, environment and security of the community, the balance of risk and reward in various options, must all be considered. Canada has lived through a few of these issues in its recent experience with science advice and advisory systems. This article will elaborate on the impact and influence of changes in science advisory bodies at the federal and Quebec government levels and will provide a perspective on their impact. It examines the historical evolution of the advisory apparatus for science throughout Canada’s history and underscores some of their successes and failures under different regimes. The conclusion drawn in this article is that science and science advisory systems in Canada have lacked continuity and a solid foundation thus weakening efforts to enable sound science-based policy into decision-making. The article argues for a more institutionalized and pluralistic approach to ensuring that evidence and science advice can endure—both at the federal and provincial levels. In many ways, the experience with these advisory mechanisms suggests a growing need to ensure sound advice within increasingly complex decision-making as well as a demand by citizens to have scientific evidence considered more carefully in public policy and for the public interest. This article is published as part of a collection on scientific advice to governments.
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 | 0.037 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.066 | 0.032 |
| Scholarly communication | 0.025 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 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".