Waking Ned Devine: une traduction franco acadienne pour locuteurs acadiens, franco-canadiens, et francophones d’ailleurs et partout
Bibliographic record
Abstract
The following study aimed to show the need for Franco-Canadian dubbings outside of France, and so as to elaborate the existing issues in audiovisual translation, the film Waking Ned Devine (2001) was translated into Acadian French (with Chiac components). The main goal of this study was to demonstrate that the universal French language used in French and Quebec dubbings is insufficient and often misunderstood by a vast majority of Franco-Canadian publics and Acadian publics in particular. The project thus includes an analysis of Ireland and Acadie’s sociocultural and sociolinguistic contexts as well as a portion of the translated film script written by Kirk Jones (1999). The translation was completed with the idea that a dubbing could eventually be produced with Acadian actors. During the research for this present memoir, a certain paradox became quite obvious: cinematographic production companies require that dubbings be made into standardized French, as much in France as in Quebec, and specifically ask that all forms of dialects and regionalisms be avoided. The result; most French dubbings are rigid, and almost artificial even, since Francophone publics do not speak this same neutral French which seems to come out of nowhere. This means, and is also due to a law put in place in France to protect French audiovisual translation rights, that a tremendously large number of American or Foreign films are dubbed twice into two standardized versions of French (in France and in Quebec) which are nearly identical and quite often despised by their targeted audiences.
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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 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".