Maria Chapdelaine vue dâailleurs: les agents et les enjeux derrière la traduction espagnole et catalane dâun classique de la littérature canadienne-française
Bibliographic record
Abstract
Le présent article vise à analyser les différentes rééditions des traductions espagnole et catalane d'un classique de la littérature canadienne-française, soit Maria Chapdelaine.À partir des préfaces conçues en tant que prises de position (au sens bourdieusien), sont analysés les différents agents derrière l'initiative de faire traduire ce roman en Espagne et en Catalogne, ainsi que le rapport de ces agents envers l'altérité canadienne-française du texte source.Est révélée la fonction idéologique des préfaces en tant que dispositifs discursifs qui permettent aux agents des champs cibles de réveiller certains débats qui vont (thématiquement et chronologiquement) bien au-delà des questions liées au texte original.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".