« j’allumions la mèche de nos langues » / « this book isn’t written in English » : les traductions postlingues de Georgette LeBlanc et de Dominique Bernier-Cormier
Bibliographic record
Abstract
Résumé : Cet article analyse, à partir d’une perspective « postlingue », deux textes publiés en Acadie ces dernières années qui remettent en question le régime monolingue, normatif de la traduction, soit la traduction Océan , signée par Georgette LeBlanc (2019), et le recueil de poésie Entre Rive and Shore, de Dominique Bernier-Cormier (2023). Une lecture postlingue de ces deux textes nous amène à redéfinir l’écriture et la traduction au-delà de l’idée de langue et à y accueillir, plutôt, l’hétérogénéité constitutive du langage. En refusant de s’adresser aux lecteur·rices par le biais d’une langue nationale soi-disant commune et transparente, LeBlanc et Bernier-Cormier montrent qu’il existe d’autres socles que « la » langue sur lesquels fonder nos rapports les uns avec les autres et que l’incommensurabilité, plutôt que d’être un obstacle à la relation, peut en être le fondement même.
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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 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".