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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it