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Record W2267047021 · doi:10.30950/jcer.v12i1.725

European Governance of Citizenship and Nationality

2016· article· en· W2267047021 on OpenAlexafffund
Willem Maas

Bibliographic record

VenueJournal of Contemporary European Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsYork University
FundersEuropean University InstituteYork University
KeywordsCitizenshipEuropean unionPolitical scienceMember stateSovereigntyAutonomyCorporate governanceJurisprudenceLawState (computer science)Law and economicsPoliticsSociologyMember statesBusinessInternational trade

Abstract

fetched live from OpenAlex

The ability of a state to determine who its citizens are is a core element of sovereignty, yet even in this area coordination in the European Union has arisen as member states adjust their policies regarding citizenship acquisition and loss to take into account the European project. Furthermore, EU citizenship grants extensive rights that member states must respect, though the only way to become an EU citizen and acquire these rights remains through citizenship of a member state. This article sketches the development of EU citizenship from the 1950s to the present, mapping its evolution onto the phases of European governance utilised in this special issue. The search for closer coordination and common guidelines concerning citizenship flows from functional needs inevitably generated by superimposing a new supranational political community over existing national ones, resulting in shared governance within the framework of member state autonomy. Though welfare states and social systems in Europe remain national and jurisprudence safeguards the ability of member states to exclude individuals despite shared EU citizenship, legal judgments emphasise that member state competence concerning citizenship must be exercised in accordance with the Treaties and that member state decisions about naturalisation and denaturalisation are amenable to judicial review carried out in the light of EU law.

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)0.018
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.789
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.186
GPT teacher head0.405
Teacher spread0.219 · 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

Citations26
Published2016
Admission routes2
Has abstractyes

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