Translating the Québécois Sociolect for Cinema: \nThe Creation of a Supertext in Bon Cop Bad Cop
Bibliographic record
Abstract
Abstract \nTranslating the Québécois Sociolect for Cinema: \nThe Creation of a Supertext in Bon Cop Bad Cop \n \nJo-Anne Hadley \n \n \nThis thesis addresses the subtitling of the Québécois sociolect into English. In the bilingual comedy film Bon Cop Bad Cop, although the meaning of the Québécois dialogue is rendered, the flavour of the original sociolect is lost. This thesis posits that the failure to render the Québécois sociolect in the English subtitles of Bon Cop Bad Cop gives rise to understatement, thereby creating a “supertext” for bilingual movie-goers who read the subtitles that actually enhances the humour of this comedy. Examples of the supertext will be drawn from Bon Cop Bad Cop to illustrate this hypothesis. Dialogue and subtitles from Bon Cop Bad Cop will be compared and contrasted with those from De père en flic/Father and Guns, a film whose subtitles successfully render the Québécois sociolect and thus do not create a supertext. This analysis will serve to determine the types of adaptation and compensation that were used in Father and Guns to render the sociolect. The findings from this project will be of interest not only to subtitlers but all translators of the Québécois sociolect.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".