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Record W2799624650 · doi:10.3917/futur.422.0087

Catalogne, Kurdistan, Écosse, quel droit à l’indépendance ?

2018· article· fr· W2799624650 on OpenAlexaboutno aff
Jean-François Drevet

Bibliographic record

VenueFuturibles · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceGeographyArt

Abstract

fetched live from OpenAlex

La crise politique que traverse l’Espagne, suite au référendum d’autodétermination organisé par la Catalogne puis à la mise sous tutelle de cette communauté autonome jusqu’aux élections régionales du 21 décembre 2017 (notre numéro était bouclé avant leur tenue), a contribué à raviver le débat sur les indépendances possibles de certains territoires européens. Le Brexit l’avait ouvert au travers des questions relatives au statut de l’Irlande du Nord ou de l’Écosse ; et le même débat resurgit régulièrement concernant Wallons et Flamands en Belgique, la Corse en France, etc. Cette première tribune européenne de 2018 vise donc à faire le point sur le droit à l’indépendance tel qu’on peut aujourd’hui l’appréhender dans le cadre de l’Union européenne pour des territoires tels que la Catalogne, le Kurdistan ou l’Écosse. Jean-François Drevet s’appuie sur les textes et la jurisprudence de l’Union, ainsi que sur les expériences antérieures, en Europe (Balkans, par exemple) ou outre-Atlantique (Québec). Au-delà des différences d’appréciation selon la communauté concernée (« deux poids deux mesures ? »), il montre bien la complexité de telles questions et la naïveté de ceux qui estiment que l’échelon européen pourrait accélérer ou simplifier le règlement des revendications indépendantistes régionales sur le Vieux Continent.

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)0.002
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.011
Scholarly communication0.0080.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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