A more social science: barriers and incentives for scientists engaging in policy
Bibliographic record
Abstract
Scientists are increasingly called upon to engage in policy formulation, but the literature on engagement is strong on speculation and weak on evidence. Using a survey administered at several broadly “ecological” conferences, we investigated: (1) the extent to which respondents engage in policy‐related activities (including reporting scientific results, interpreting science for policy makers, integrating science into decision making, taking a position on a policy issue, and acting as a decision maker); (2) what factors best explain these types of engagement; and (3) whether respondents' activity levels match their stated beliefs on such activities. Different factors explain different forms of participation. Past negative experience was identified as a barrier to taking part in policy, while self‐perceived competence in navigating the science–policy interface was consistently important in explaining activity across all engagement types, highlighting the importance of training programs linking scientists to policy. Many respondents believed that scientists should interpret, integrate, and advocate, which contrasts with previous research and relatively low levels of self‐reported participation in 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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".