The semiotic puzzle: Authentic languages & international law
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.083 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".