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Record W3043166306 · doi:10.31861/mhpi2020.41.46-57

Ukraine and Canada: The European Union as a Partner in Negotiation

2020· article· en· W3043166306 on OpenAlexaboutno aff

Bibliographic record

VenueModern Historical and Political Issues · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and International Law Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The European Union as a mode of European Unification is a good example of an international actor channelling negotiation processes in an optimal way. As both Ukraine and Canada will continue and probably intensify negotiation processes with the European Union it seems to be relevant to take a closer look at the EU as a negotiation partner and opponent. Negotiating with the EU is very complicated, first of all because of the complexity of the EU itself. The European Union is, compared to other collective international actors, a strong transnational organization with international and supranational features. This strength has an impact on the negotiation process and its closure. It is special in the sense of having a strong legal system with the European Court with powers to enforce compliance on the Member States. It”s institutions have their own role to play and cannot be ignored. The architecture of the Union consists of a wide range of actors, issues and thereby processes, having consequences for the EU citizens, their governments and those of other countries in Europe and the world, like Ukraine and Canada. The European Union is an actor in its own right on the world stage. As a hybrid international construct – being neither a state, nor a conventional international organization, nor a full supranational body – the EU is a power block that is difficult to be handled. It is a problematic entity, for itself and for third countries. This paper analyses the character and characteristics of some of the key internal and external negotiation processes of the EU, as they have been influenced by the strengths and weaknesses of the organization. Understanding its internal negotiation complexity will help Canada and Ukraine to conduct successful negotiations.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.178
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0170.008
Scholarly communication0.0160.005
Open science0.0010.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.037
GPT teacher head0.282
Teacher spread0.246 · 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 designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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