A Collaboratively-Derived Science-Policy Research Agenda
Bibliographic record
Abstract
The need for policy makers to understand science and for scientists to understand policy processes is widely recognised. However, the science-policy relationship is sometimes difficult and occasionally dysfunctional; it is also increasingly visible, because it must deal with contentious issues, or itself becomes a matter of public controversy, or both. We suggest that identifying key unanswered questions on the relationship between science and policy will catalyse and focus research in this field. To identify these questions, a collaborative procedure was employed with 52 participants selected to cover a wide range of experience in both science and policy, including people from government, non-governmental organisations, academia and industry. These participants consulted with colleagues and submitted 239 questions. An initial round of voting was followed by a workshop in which 40 of the most important questions were identified by further discussion and voting. The resulting list includes questions about the effectiveness of science-based decision-making structures; the nature and legitimacy of expertise; the consequences of changes such as increasing transparency; choices among different sources of evidence; the implications of new means of characterising and representing uncertainties; and ways in which policy and political processes affect what counts as authoritative evidence. We expect this exercise to identify important theoretical questions and to help improve the mutual understanding and effectiveness of those working at the interface of science and policy.
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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 | 0.370 | 0.231 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.042 | 0.060 |
| Open science | 0.014 | 0.059 |
| Research integrity | 0.057 | 0.037 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".