Traduire le sociolecte des chauffeurs égyptiens d’après <i>Taxi</i> de Khaled Al Khamissi
Bibliographic record
Abstract
Le transfert des faits dialectaux dans une langue-culture étrangère s’avère difficile, notamment si les deux langues-cultures en question ne partagent pas le même socle culturel. L’objectif de cette étude est d’analyser les problèmes du transfert d’un texte arabe dialectal en langue française. À partir d’un roman écrit en arabe, oscillant entre l’arabe standard moderne et l’arabe égyptien, nous allons tenter d’étudier la traduction vers la langue française de quelques faits dialectaux composant le sociolecte des chauffeurs de taxi égyptiens. Pour ce faire, nous le classifierons en trois catégories : proverbes dialectaux, expressions figées dialectales et expressions dialectales inspirées de la religion. Nous voulons examiner les stratégies traductives dont se servent les traducteurs pour en transférer les composants en langue française. Les exemples d’analyse portent sur Taxi, roman arabe écrit par Khaled Al Khamissi, et sa traduction en français, ayant le même titre, faite par Hussein Emara et Moîna Fauchier Delavigne.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".