Learning to deploy civilian capabilities: How the United Nations, Organization for Security and Co-operation in Europe and European Union have changed their crisis management institutions
Bibliographic record
Abstract
International organizations continuously deploy civilian capabilities as part of their peacekeeping and crisis management operations. This presents them with significant challenges. Not only are civilian deployments rapidly increasing in quantity, but civilian missions are also very diverse in nature. This article analyses how international organizations have learned to deploy their civilian capabilities to deal with a growing number and fast evolving types of operations. Whereas the previous literature has addressed this question for individual international organizations, this article uniquely compares developments in the United Nations (UN), European Union (EU) and Organization for Security and Co-operation in Europe (OSCE), three of the largest civilian actors. Drawing on the concept of organizational learning, it shows that all three organizations have made significant changes over the last decade in their civilian capabilities. The extent of these changes, however, varies across these organizations. The article highlights that the EU, despite its more homogeneous and wealthier membership, has not been able to better learn to deploy its civilian capabilities than the UN or OSCE. We show that the ability of these organizations to learn is, instead, highly dependent on institutional factors.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".