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Record W307442923

A Giant with Feet of Clay? The EU's Ability to Develop Capabilities for Civilian Crisis Management

2013· article· en· W307442923 on OpenAlexaff
Rafał Domisiewicz

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsCarleton University
Fundersnot available
KeywordsContext (archaeology)Corporate governanceCrisis managementIdentity (music)State (computer science)Political scienceSecurity policyMember stateBusinessEconomic systemEuropean unionPublic administrationPolitical economyMember statesEconomic policyEconomicsGeographyLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.274
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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