Navigating the science policy interface: a co-created mind-map to support early career research contributions to policy-relevant evidence
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
The interface between science and policy is a complex space, in theory and practice, that sees the interaction of various actors and perspectives coming together to enable policy-relevant evidence to support decision-making. Early Career Researchers (ECRs) are increasingly interested in working at the science-policy interface to support evidence-informed policy, with the number of opportunities to do so increasing at national and international levels. However, there are still many challenges limiting ECRs participation, not least how such a complex space can be accessed and navigated. While recommendations for engaging at the science-policy interface already exist, a practical 'map' of the science-policy interface landscape which would allow for ECR participation in evidence co-production and synthesis in science-policy is missing. With the purpose of facilitating the engagement of ECRs producing biodiversity and ecosystem services policy-relevant evidence at the interface between science and policy, the authors have co-created a 'mind-map'-a tool to review the landscape of and leverage access to the science-policy interface. This tool was developed through reviewing published literature, collating personal experiences of the ECR authors, and validating against wider peer perspectives in an ECR workshop during the 7th Plenary of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). This co-created tool sees ECR engagement in (co-)producing evidence at the science-policy interface as an interaction of three main factors: the environment of the ECR, which mediates their acts of engagement at the science-policy interface leading to outcomes that will ultimately have a reciprocal impact on the ECR's environment.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it