A Giant with Feet of Clay? The EU's Ability to Develop Capabilities for Civilian Crisis Management
Bibliographic record
Abstract
Civilian crisis management has long been considered the EU's forte. Recent research however has questioned the EU's claim to this specialization. I will interrogate how the EU has fared in building civilian capabilities for CSDP through a case study of the impact of the Europeanization of CCM norms in one of the newer EU member states - Poland. I investigate the domestic reverberations of an EU-level CCM governance - conceptualized as a vertical diffusion of norms - and a horizontal diffusion in the realms of policy setting, institutional adaptation, as well as in recruitment and training. I hypothesize that the European cognitive constructions and policy designs are the more likely to impact upon Polish security policy the more they resonate with the ideas embedded in the national security identity. Another intervening variable affecting the 'translation' of EU policy into the domestic context is state capacity. Due to weaknesses in the supply side of CCM and the refracting impact of national security identity and state capacity, I find that Europeanization has had a limited impact on the civilian response capability-building in Poland. Europeanization has been shallow, featuring adjustments on the margins rather than the core of the security policy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".