Making the Bedouins: Code-Switching as Model for the Translation of Multilingual Drama
Bibliographic record
Abstract
The translation of theatre from one linguistic and cultural context to another can be uniquely challenging; these challenges are multiplied when the source text is itself multilingual. René-Daniel Dubois’s Ne blâmez jamais les Bédouins, translated into English under the name Don’t Blame the Bedouins by Martin Kevan, unfolds in English, French, Italian, German, Russian, and Mandarin. The original “French” text presents as postdramatic, deconstructing language and identity in a sometimes frenetic pastiche. Kevan’s “Anglophone” text, however, resists the postdramatic deconstruction in the original, instead bulking up Dubois’ macaronic and archetype-heavy collage with some attempts at psychological depth. Because of its polyglossic complexity and because it has been translated, published, and produced in both English and French, it proves an excellent case study that allows for an in-depth analysis of how multilingual theatrical translation can be carried out. I propose that Kevan’s translation of Dubois’ play exhibits not only textual and performative translation, but that he also translates the linguistically-coded aesthetic conventions that distinguish Quebecois and English Canadian drama and their respective audiences. Kevan shows sensitivity to the gap between the politics of language in French and English Canada as well as to the gap between theatrical codes in both linguistic communities by amplifying the psychological realism and consequently tempering the language politics in his “English” version of Dubois’s work. The choices that Kevan made in his translation are here elucidated by borrowing linguistic theories of conversational code-switching to analyze both versions of the play.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".