La traduction juridique dans la francophonie : éléments de comparaison franco-canadiens en matière de droit pénal
Bibliographic record
Abstract
Dans ce mémoire, nous avons tenté d’expliquer les raisons pour lesquelles des différences de traduction peuvent survenir si tant est que l’on traduit vers le français du Canada ou vers le français de France, et de déterminer des stratégies permettant au traducteur de surmonter les difficultés que cela peut engendrer. Nous avons pris l’exemple du Code criminel canadien, en comparant des termes issus de la version française du Code à des traductions proposées en français de France. Nous avons de plus abordé certains aspects théoriques de la traduction juridique, en nous intéressant notamment aux approches fonctionnelles de la traduction et à la théorie du skopos, ainsi qu’à différentes typologies de textes juridiques. Nous avons également adopté une approche comparative des systèmes juridiques français et canadien.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".