Navigating the science policy interface: a co-created mind-map to support early career research contributions to policy-relevant evidence
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.
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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 | 0.069 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.031 | 0.033 |
| Open science | 0.005 | 0.044 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".