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Record W2511161685 · doi:10.17645/pag.v4i3.598

The Federal Features of the EU: Lessons from Canada

2016· article· en· W2511161685 on OpenAlexaffabout
Amy Verdun

Bibliographic record

VenuePolitics and Governance · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFederalismPolityPolitical scienceContext (archaeology)PoliticsEuropean unionPublic administrationWork (physics)IdeologyNew FederalismPolitical economyLawSociologyGeographyEconomic policyBusinessEngineering

Abstract

fetched live from OpenAlex

There has been a rise and fall in interest in federalism in the context of European integration. This article assesses the federal nature of the EU. It draws in particular on the work of Michael Burgess who has been one of the key thinkers on this issue. Because there are many types of ‘federalisms’ available across the globe, it is helpful to make a comparison with another political system to offer a base line. In this article I explore to what extent the EU already has federal features. With the help of the work of Burgess I seek to look beyond the specific characteristics of the EU and reflect on how a comparison with this other polity can offer us insights into what is going on within the EU political system. Drawing on the comparison with Canada, I seek to identify the characteristics of the EU that are already those of a federation. Therefore, the guiding question of this article is: compared to Canada, what particular features does the EU have that reminds us of a federation and what features is it still lacking? It finds that the EU has a considerable amount of federal features (federation), but that a federal tradition, a federal ideology and advocacy to a federal goal (federalism) are mostly absent.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.134
Threshold uncertainty score0.971

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.007
Science and technology studies0.0170.005
Scholarly communication0.0100.003
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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 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

Citations12
Published2016
Admission routes2
Has abstractyes

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