Institutional Change Through Policy Learning: The Case of the European Commission and Research Policy
Bibliographic record
Abstract
Abstract Research initiatives to enhance knowledge‐based societies demand regionally coordinated policy approaches. By analyzing the case of the European Commission, Directorate‐General Research and Innovation, this study focuses on examining the cognitive mechanisms that form the foundation for institutional transformations and result in leadership positions in regional governance. Drawing on policy learning theories, the study emphasizes specific mechanisms of institutional change that are often less noticeable but can gradually lead to mobilizing diverse groups of stakeholders. Through historical and empirical data, this study shows the importance of policy learning through communication processes, Open Method of Coordination initiatives, and issue framing in creating a stronger foundation for policy coordination in European research policy since the 2000s.
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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 | 0.061 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.019 | 0.035 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.016 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".