Learning in International Governmental Organizations: The Case of Social Protection
Bibliographic record
Abstract
There exists considerable research on how national policy makers learn from abroad. A significant amount examines the processes and actors at work at the international level. In that strand, relatively little attention has gone to international governmental organizations (IGOs), aside from the European Union (EU)'s Open Method of Coordination. In this article, we carry out a comparative study of learning in three IGOs: the EU, the Organisation for Economic Co-operation and Development, and the Nordic Council of Ministers. Our policy area is social protection. We investigate what is being learned, and the factors that promote or block learning. Our methodology involves an analysis of the formal design of those IGOs and face-to-face interviews with high-ranking bureaucrats from each organization. We observe, first, that the most important learning in IGOs concerns matters that are not part of formal agendas - governance and epistemic issues above all. Second, we see that very different factors promote or block learning in different organizations. We reflect on the implications of these findings for both theory and practice. © The Author(s), 2010.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.061 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".