Engaging Scientists in Science Policy: Experiences From Canada
Bibliographic record
Abstract
Background: Training scientists in science policy is crucial to enhance scientific advice for decision-making. However, there are limited opportunities for scientists to receive such training. Analysis: We reflected on our participation in a one-year postgraduate science policy fellowship program in Canada. Although recently discontinued in 2023, this fellowship allowed us to refine practical policy skills, contribute to policy outputs that advanced our office's mandate, and access career pathways beyond academia. Policy Options: Recognizing the value of engaging scientists in policy, we advocate for continued offerings of science policy training, alongside rigorous evaluation to inform program changes. Additionally, we encourage increased financial support early on for graduate students to sustain a talent pool of scientists who will become future science policy leaders. Lastly, we urge more scientists and students to be active in science policy spaces. Conclusion: By openly sharing our experiences and learnings from the fellowship, we seek to contribute to ongoing discussions on the importance of science policy training and its role in bridging the gap between science and 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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.055 | 0.016 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".