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Record W3114910121 · doi:10.1515/ijld-2020-2039

The semiotic puzzle: Authentic languages & international law

2020· article· en· W3114910121 on OpenAlexaff
Clara Chapdelaine-Feliciati

Bibliographic record

VenueInternational Journal of Legal Discourse · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsYork University
Fundersnot available
KeywordsTreatyLawMeaning (existential)Interpretation (philosophy)Legal translationHuman rightsConventionPolitical scienceLinguisticsInternational lawContext (archaeology)SociologyHistoryPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Abstract The principle of the equal authority of authentic languages enshrined in the Vienna Convention on the Law of Treaties (1969) has created significant debates in the interpretation of multilingual treaties. In this context, the present article explores the complex ramifications of the legal translation of human rights treaty provisions and the “translatability” and transposition of legal concepts into other linguistic frameworks. It considers whether a semiotic analysis of the content of UN international human rights treaties conducted in a single authentic language, English or French, has a raison d’être , in light of Victoria Welby’s Threefold Laws of Meaning . The article further assesses whether the Sense and Significance of treaty provisions will differ in distinct languages. It begins by examining the important role attributed to English and French at the international level. Secondly, it studies the problem of the variations between the meaning(s) of provisions enshrining rights in two or more authentic languages, namely Arabic, English, Chinese, French, Russian and Spanish, and which interpretation should prevail under the Vienna Convention . For this purpose, it considers problems that arise expressly in English and French by conducting a comparative study of these languages with the Spanish and Chinese texts of the International Covenant on Civil and Political Rights (1966).

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)0.001
metaresearch head score (Gemma)0.000
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 score0.960
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.369
Teacher spread0.347 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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