Translating Ferron, Ferron Translating: Thoughts on an Example of "Translation Within"
Bibliographic record
Abstract
Dans le texte de La nuit (1965) et de sa version "corrigée", Les confitures de coings(1972), Jacques Ferron insère une traduction française d'un poème anglais de SamuelButler, attirant ainsi implicitement l'attention sur l'acte de traduire. S'élabore en même temps, dans la narration et les dialogues adjacents, une thématisation explicite de cet acte, qui se trouve, par le fait même, soumis à un questionnement critique. Quelle est la fonction de la traduction quand celle-ci s'effectue entre cultures "inégales" ? Quels textes traduit-on ? Qui les choisit? Qui réalise les traductions? Celles-ci serviraient-elles des fins précises ? La traduction est ainsi problématisée. Mais quel sens ce questionnement revêt-il pour le traducteur anglais? Comment celui-ci situera-t-il son travail par rapport à une telle remise en question de la traduction de la part de l'auteur lui-même? Telle est la question qui sous-tend ma lecture de la traduction inscrite au cœur de ces deux textes.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".