Comment le sous-titrage et le doublage peuvent modifier la perception d’un film
Bibliographic record
Abstract
Avec les longs métrages célèbres, devenus même parfois films-cultes, la question de la traduction (principalement sous la forme de versions sous-titrées ou doublées) se pose de façon d’autant plus pertinente que les critiques et les spectateurs ont souvent tendance à amalgamer film original et versions traduites. Il semble donc important de réévaluer ces traductions et leur influence sur la réception d’un film. À partir d’une analyse contrastive détaillée de la version originale du film de Kazan, A Streetcar Named Desire (1951) et deux de ses versions (sous-titrée et doublée en français), je me propose d’exposer les répercussions des choix respectifs faits par les traducteurs sur deux aspects majeurs dans la signification esthétique et symbolique du film, à savoir la présentation des personnages et leurs relations ainsi que les questions de transferts culturels.
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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 | 0.003 | 0.009 |
| 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 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".