Rethinking the Open Method of Coordination: Mutual Learning Initiatives Shaping the European Research Enterprise
Bibliographic record
Abstract
Since 2000, the Open Method of Coordination (OMC) has become a policy approach increasingly used in the European policy making process. By focusing on research policy, this study examines the ways in which the OMC and the mutual learning initiatives have influenced the wider policy discourse in the European Union. The paper argues that it is important to think about the contributions of the OMC in research policy in more broad and fundamental ways. This theory-guided study takes an empirical approach to the OMC, providing significant evidence on mutual learning effects analyzed in terms of developing an authentic dialogue, shaping policy discourse, shaping policy networks and facilitating collaborative learning. The analysis reveals that the OMC changes the ways in which the representatives from the Member States and the European Commission contribute to research policy, leading to a promising foundation for further policy enhancement. Full text available at: https://doi.org/10.22215/rera.v7i2.218
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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 | 0.195 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.017 | 0.098 |
| Scholarly communication | 0.035 | 0.033 |
| Open science | 0.004 | 0.046 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".