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Record W4388482870 · doi:10.1093/ia/iiad268

European Union communities of practice: diplomacy and boundary work in Ukraine

2023· article· en· W4388482870 on OpenAlexaffabout
Emanuel Adler

Bibliographic record

VenueInternational Affairs · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDiplomacyEuropean unionBoundary-workWork (physics)Political scienceSoviet unionBoundary (topology)Library sciencePublic administrationSociologySocial scienceLawEngineeringInternational tradeEconomicsPoliticsMechanical engineering

Abstract

fetched live from OpenAlex

In this book, Maren Hofius embarks on the ambitious task of reimagining International Relations (IR) practice theory, community and the European Union. She suggests a theory of how international communities are possible despite heterogeneity, internal differences and a weak sense of ‘we’. The book starts with a compelling critique of normative and functionalist concepts of community that are common in the IR literature. According to the author, these overlook the deep-seated practices and underpinnings that structure communities. Hofius offers a corrective lens and argues that communities should be understood as a dynamic layer of communities of practice. She conceives the latter as a ‘context within which modes of belonging … are negotiated through the enactment and reification of shared practices’ (p. 6). Mutual engagement, the negotiation of a joint enterprise and a repertoire of shared resources, which characterize communities of practice, enable the emergence of a shared identity notwithstanding cultural and national diversity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0050.008
Scholarly communication0.0100.008
Open science0.0010.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.024
GPT teacher head0.371
Teacher spread0.347 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2023
Admission routes2
Has abstractyes

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