Science advice at the top: a global overview of chief science advisor model in governance
Bibliographic record
Abstract
Abstract Science advice plays a key role in policymaking, with governments adopting various models to integrate expertise into decision-making. This study provides a preliminary overview of the Chief Science Advisor (CSA) model, examining its adoption across different governance structures. Our mapping analysis identifies seven countries—the US, the UK, Canada, Australia, New Zealand, India, and Ireland—that have formally institutionalized this model, while also exploring cases where it has been discontinued or never fully formalized. While the CSA is not the sole mechanism for science advice, it offers a distinct approach that balances expert guidance with political realities. Through qualitative analysis of expert interviews with former CSAs, public officers, and policy experts, we examine the professional backgrounds, competencies, and strategic roles of CSAs as well as their agenda. By assessing both the strengths and limitations of this advisory structure, this study contributes to discussions on enhancing evidence-informed governance and public trust in decision-making.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".