Vers un apprentissage semi-autonome du processus de la traduction
Bibliographic record
Abstract
Il est difficile d’entamer le débat sur l’évaluation de la traduction si on ne précise pas dans quel contexte (professionnel ou didactique) celle-ci va être pratiquée ou quelle est sa fonction exacte (stimuler les apprenants à perfectionner leurs compétences et performances ou sanctionner un produit final). Cet article contient, donc, une description du cadre local dans lequel nous avons développé une méthode d’évaluation qui met à profit les avantages de tout un éventail d’outils électroniques (en premier lieu, le logiciel d’annotation Markin) et qui tente de concilier les exigences de l’évaluation formative et de l’évaluation sommative, tout en privilégiant la première. Le contexte didactique conditionne les réponses qu’on donne à des questions classiques, comme celles qui ont trait au statut de la faute de langue dans les cours de traduction, ou à l’importance relative de la cause et de l’effet des erreurs et des fautes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".