« Renoi, t’as besoin que je te l’explique ? »1 Les stratégies de traduction des termes d’adresse dans le doublage et le sous-titrage de The Wire
Bibliographic record
Abstract
À partir d’un corpus constitué d’extraits de onze épisodes de la première saison de The Wire, une télésérie américaine mettant en scène des protagonistes des milieux populaires et criminels de la ville de Baltimore, nous avons examiné les stratégies de traduction des marqueurs de la relation interpersonnelle utilisées dans le sous-titrage et le doublage en français de la bande-son originale anglaise. Nous avons ainsi envisagé comment le niveau de la relation interpersonnelle est modifié ou non par le processus de traduction. Nous avons essentiellement ciblé la traduction des termes d’adresse afin d’identifier les stratégies (registre de langue, ponctuants, locutions, expressions idiomatiques) mises en oeuvre pour traduire des expressions anglaises telles que man et nigger qui, en plus de leur valeur linguistique, ont une valeur pragmatique importante ainsi qu’une valeur identitaire forte.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".