Rethinking evaluation of science-for-policy ecosystems
Bibliographic record
Abstract
Three years ago, the EU Joint Research Centre launched a series of thematic workshops focused on mapping and strengthening national ecosystems of science-for-policy. A national ecosystem of science-for-policy consists of an interlinked set of institutions, structures, mechanisms, and functions that interact at different levels to provide scientific evidence for policy. The workshops were meant to showcase the national composition as well as cross-national similarities of the advisory ecosystems connecting science with policy. The dialogue demonstrated how EU Member States use a wide range of structures and instruments to provide evidence and advice to policymakers, often connected through a complex set of institutions. In this blogpost for the European Commission, Prof. David Budtz Pedersen looks back at the lessons learnt and ahead towards a new framework for evaluation of science-for-policy ecosystems.
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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 | 0.520 | 0.469 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.080 | 0.061 |
| Open science | 0.010 | 0.038 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".