didactique de l’erreur dans l’apprentissage de la traduction
Bibliographic record
Abstract
The notion of mistake is often central to our perception of translation training, as can be seen in the glossaries of translation manuals. This vision of translation is also evident in the way teachers assess students’ work: their role often consists entirely in correcting mistakes. And yet, as some specialists in didactics point out, learners may be anxious and stressed by the fear of committing mistakes, a situation which is not propitious to learning. But beyond the mere notion of correction of mistakes, which may be inhibiting and considered as emphasising failure, mistakes or errors may be used as a substructure leading to ‘rebuilding’ of knowledge. An error may be a valuable educational tool, but it must be used with the greatest caution. In translation training, a teacher will often be able to help students make progress only if he/she is aware of the type of errors students are prone to making: it thus may be very useful to correct errors then to offer guidance to learners so that they understand the source of their errors in order to avoid their recurrence.
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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".