Tarantino’s Inglourious Basterds: a blueprint for dubbing translators?
Bibliographic record
Abstract
Released in 2009, Quentin Tarantino’s Inglourious Basterds 1 is representative of a recent trend of multilingual films in Hollywood. The inclusion of one or several languages other than English can be problematic for dubbing translators when the films is exported. The comparison between the original version of Inglourious Basterds and its French dubbed version offered in this article brings to light a number of translation issues. The idea of the codified relationship with the audience leads us to explore notions of conventions, contrivances, and suspension of disbelief, whether in the native language or original subtitling included in the American version. Dubbing is not only translating, it raises the issue of the texture of the original voices, especially in a film preoccupied with accents. Finding French voices with an equivalent texture but which are also plausible for the audience is a challenge that the dubbing team must meet. Finally, the vital importance of languages in the narrative and thematic construction of Tarantino’s film result in the inevitable loss due to the dubbing process. This article is not an attack on the dubbing process, but an attempt to interrogate its complexity and determine its role.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| 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".