La importancia de la investigación colaborativa en la pedagogía de la traducción
Bibliographic record
Abstract
Au cours des vingt dernières années, des auteurs tels que Delisle (1988) et Kiraly (2000) se sont inquiétés de l’absence d’un consensus quant aux méthodes d’enseignement des cours pratiques de traduction. Dans cet article, nous faisons premièrement une révision du concept de communauté en traductologie et deuxièmement, nous nous faisons l’écho des critiques de Delisle quant au manque d’innovation en méthodologie de l’enseignement dans les cours pratiques de traduction. Troisièmement, nous offrons deux exemples de recherches qui peuvent être menées au sein des cours. Ce sont des initiatives pour encourager la recherche collaborative en traduction et surtout pour insister sur l’importance de la salle de classe comme le lieu par excellence de la production des données empiriques sur lesquelles fonder une méthodologie de l’enseignement de la traduction.
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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 | 0.120 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".