A more social science: barriers and incentives for scientists engaging in policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it