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Plurijuridismes, juges suprêmes et droits fondamentaux : étude comparée entre l'Union Européenne et le Canada

2015· dissertation· W7147524017 on OpenAlexaboutno aff
Aurélie Laurent

Bibliographic record

Venuenot available
Typedissertation
Language
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsFree accessGood faithClientelism

Abstract

fetched live from OpenAlex

Les juges sont aujourd’hui des acteurs indispensables : garants des droits et libertés fondamentaux et arbitres des relations entre les ordres juridiques, ils exercent des missions essentielles qu’il n’est pas toujours aisé de concilier. Cette étude comparative entre l’Union européenne et le Canada propose d’en analyser les ressorts en s’intéressant aux interactions entre un mode d’organisation juridique particulier (le plurijuridisme), un organe (une juridiction suprême) et des normes spécifiques (les droits fondamentaux). En effet, la Cour suprême du Canada et la Cour de justice de l’Union européenne sont d’abord essentielles pour accommoder un ordre juridique commun (canadien ou européen) avec la préservation d’une certaine diversité juridique (entre les États membres de l’Union européenne ou bien entre les provinces et communautés autochtones canadiennes). Elles doivent ensuite garantir les droits de la personne, ce qui implique notamment, une pluralité d’instruments de protection et des modalités d’application complexes des Chartes canadienne et européenne. Les plurijuridismes canadien et européen se trouvent toutefois bouleversés puisque la structure du contentieux des droits fondamentaux et la manière dont les juges manient les standards de protection tendent à favoriser l’unité et à engendrer une homogénéisation. Une protection substantielle des droits fondamentaux dans le respect du plurijuridisme reste pourtant possible à la faveur d’une méthode dialogique et pluraliste.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.583

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.008
Science and technology studies0.0130.007
Scholarly communication0.0100.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.016
GPT teacher head0.303
Teacher spread0.287 · 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 designQualitative
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
Published2015
Admission routes1
Has abstractyes

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