DEFIS DE LA TRADUCTION DU THEATRE QUEBECOIS CONTEMPORAIN EN ROUMAIN
Bibliographic record
Abstract
Our article provides some reflections on the challenges in translating Canadian French theatrical texts into Romanian. We start from the premise that these texts, due to their artistic purpose, which is to be performed on stage, present enunciative and construction specificities that must be considered in the translation process. This type of adaptation involves a distinct mode of reception as well as other mechanisms of interpretation, which go beyond the simple translation of a literary text. In our opinion, there are two levels in the translation of theatrical texts: the transposition of the source text into a target text and the adaptation, at the moment of the staging, of its interpretation by the actors. In addition, there is the challenge of translating cultural facts and references in the source text. Are they accessible to the target audience? If not, how can they be adapted? Lastly, there are some specificities of translating dramatic texts that concern the translator himself. He may be a professional translator, who will focus his attention on the fidelity of the translation 29 to the original, or a theatre professional (director, actor), who will concentrate mainly on the adaptation process, because he will favour stage representation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".