Keeping science’s seat at the decision-making table: Mechanisms to motivate policy-makers to keep using scientific information in the age of disinformation
Bibliographic record
Abstract
Policy-makers are confronted with complex problems that require evaluating multiple streams of evidence and weighing competing interests to develop and implement solutions. However, the policy interventions available to resolve these problems have different levels of supporting scientific evidence. Decision-makers, who are not necessarily scientifically trained, may favour policies with limited scientific backing to obtain public support. We illustrate these tensions with two case studies where the scientific consensus went up against the governing parties’ chosen policy. What mechanisms exist to keep the weight of scientific evidence at the forefront of decision-making at the highest levels of government? In this paper, we propose that Canada create “Departmental Chief Science Advisors” (DCSAs), based on a program in the UK, to help complement and extend the reach of the newly created Chief Science Advisor position. DCSAs would provide advice to ministers and senior civil servants, critically evaluate scientific work in their host department, and provide public outreach for the department’s science. We show how the DCSAs could be integrated into their departments and illustrate their potential benefits to the policy making process and the scientific community.
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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 | 0.173 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.020 | 0.044 |
| Scholarly communication | 0.031 | 0.018 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.022 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".