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Record W2031526536 · doi:10.1163/15718115-01903006

Quiet Diplomacy, the European Union and Conflict Prevention: Learning from the HCNM on Issues of Social Cohesion

2012· article· en· W2031526536 on OpenAlexaff
Michael Johns

Bibliographic record

VenueInternational Journal on Minority and Group Rights · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsLaurentian University
Fundersnot available
KeywordsCohesion (chemistry)DiplomacyQUIETEuropean unionPolitical sciencePolitical economyPublic administrationSociologyLawInternational tradePoliticsEconomics

Abstract

fetched live from OpenAlex

This article examines the issue of social cohesion inside the European Union (EU). While the organisation is currently experiencing an economic crisis the article argues that issues surrounding minority rights are deeper and more troubling. Within the EU’s current and prospective borders there remain issues surrounding national minorities, traditional migrants and intra-EU migrants. The intra-EU migrants are of particular interest in that they bridge the gap between the other two groups as they are migrants – with EU protected rights. This article argues that the EU itself must become involved in the maintenance of social cohesion. It recommends the use of quiet diplomacy, best illustrated by the Organization for Security and Co-operation in Europe High Commissioner on National Minorities. After an examination of the use of quiet diplomacy by the various High Commissioners the article outlines the issues of social cohesion facing the EU. It concludes with the recommendation that the European Commission adopt the tenets of quiet diplomacy as a means of influencing dialogue and to promote minority protection within the union.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.037
GPT teacher head0.334
Teacher spread0.297 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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