Bibliographic record
Abstract
Certes, mais traduire l'herbe, l'orage, l'insecte, le serpent, le requin et la mouette en arabe est-il tout autant admissible sur le plan poétique ?Le fait que Mounin ait jugé « impossible », il y a plus de 30 ans, de traduire René Char en arabe est incontestable, surtout sur le plan de la linguistique.En effet, la linguistique a toujours dit « non » à la traduction (1) .Mais l'existence des traductions arabes de l'œuvre de René Char suscite à son tour d'autres questions : qui a « pu » le traduire ?Comment a-t-il « pu » le traduire ?Quand ?Et pourquoi ?Cet article propose une méthode consistant à analyser deux corpus : le corpus paratextuel des traductions, limité à un seul élément de l'épitexte (l'allographe officieux) et un seul élément du péritexte (la préface) ; et le corpus textuel qui est indubitablement restreint, générique et en majorité au format numérique (2) .Ce dernier sera ensuite soumis à une analyse poétique de la forme et du fond en les comparant avec ceux du texte d'origine afin d'évaluer les pertes et les gains de l'opération traduisante.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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.
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".