Recontextualising nouvelle vague cinema in Québec: Leonard Cohen, subtitler of Claude Jutra’s À tout prendre
Bibliographic record
Abstract
In this contribution, we examine the relationship between text and context in multimodal translation practices by focusing on the Québécois film À tout prendre (1963; directed by Claude Jutra) and its English version, Take It All (translated by Leonard Cohen). The first two sections provide contextual information, while Section 3 is dedicated to a comparative analysis, in which a more central role is given to the film itself and to the archival documents (such as draft versions of the French script and the English translation). On the whole, Take It All results from a complex interplay of factors: the overuse of reduction (condensation and deletion) despite the absence of spatial or temporal limitations; Cohen’s limited translation experience, combined with his influential profile as an artist; and the assumed intended target audience. The subtitles serve as an ancillary device, offering a minimalist representation of the original dialogue. Regarding the linguistic transfer itself, no misunderstandings have been encountered, although the translation can be said to diverge substantially from the original in numerous respects. Consequently, the English version exhibits a less intricate network of interrelationships and it can be argued that the subtitles have not significantly contributed to the film’s internationalization journey.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".