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Record W1554146886

Harmoniser le Droit Dans un Espace Multilingue et Pluri-Juridique: Un Point de Vue Canadien (Legal Harmonization in a Multilingual and Poly-Jural Space: A Canadian View)

2008· article· fr· W1554146886 on OpenAlexaffabout
Robert Leckey

Bibliographic record

VenueSSRN Electronic Journal · 2008
Typearticle
Languagefr
FieldSocial Sciences
TopicComparative and International Law Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsHarmonizationLawPolitical scienceLegal pluralismCivil law (Civil law)Legal researchPublic lawLegal realism
DOInot available

Abstract

fetched live from OpenAlex

In what way does Canada act a testing ground for legal harmonization in a multilingual and pluri-juridical environment? In Canada, harmonization focuses on language and separate legal traditions. The equal legal authority of the French and English versions of federal law and the laws of certain provincial legislations is of elementary importance. Where the harmonization of legal traditions is concerned, a distinction should be made between formal and informal harmonization. Informal harmonization refers to the process of exchange and mutual influence between the common law and the civil law within the country. Moreover, it is a constitutional principle in Canada that the application of federal law may vary from one province to another depending on which provincial law is in force. In short, the legal diversity springing from separate legal traditions is part of the constitutional order, and it is regarded as an advantage rather than a drawback. The federal government has made a considerable effort to adjust the laws to take into account the civil law of Quebec, and has promulgated a rule ordering that the federal laws must be interpreted so as to accommodate both legal traditions. While the Canadian experience shows that the harmonization process is never complete but an ongoing challenge, nevertheless it emphasizes the value of pluralism and the meeting of languages and legal traditions.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.012
GPT teacher head0.272
Teacher spread0.260 · 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.

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
Published2008
Admission routes2
Has abstractyes

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