Experts versus novices : l’utilisation de sources d’information pendant le processus de traduction
Bibliographic record
Abstract
Cette étude a pour objectif d'examiner l'utilisation des sources d'information au cours du processus de traduction. Trois traducteurs professionnels et trois étudiants en traduction avaient pour tâche de traduire un texte en pensant à voix haute. Les résultats révèlent une corrélation entre le nombre de sources d'information consultées, l'expérience de la traduction et la qualité de la traduction. En revanche, la qualité de la traduction n'est pas liée à la préférence pour un certain type de source d'information (par exemple, dictionnaire monolingue vs dictionnaire bilingue). Notre étude a des implications de deux ordres. Sur le plan méthodologique, il convient de rappeler que des résultats corrélationnels ne permettent pas d'établir des liens de cause à effet. Sur le plan de la pédagogie de la traduction, nos données mettent en cause la critique souvent avancée à l'égard de l'usage de dictionnaires bilingues.
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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 | 0.018 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".