Dictionnaires et traductologie : le paradoxe d’une lointaine proximité
Bibliographic record
Abstract
Bien que les dictionnaires soient les principaux instruments de travail des traducteurs, leur relation mutuelle n’a, jusqu’à présent, jamais été analysée en profondeur. L’usage que font les traducteurs des dictionnaires n’a guère fait l’objet de recherches, et le fait que les lexicographes bilingues sont en fin de compte des traducteurs eux-mêmes n’est jamais pris en compte. C’est cette question qui est soulevée par le présent article, lequel fournit une première ébauche de ce type de recherches. Si l’on compare les traductions de quelques phrases-exemples dans trois dictionnaires bilingues anglais-français, les problèmes rencontrés par les lexicographes ne paraissent pas tellement différents de ceux des traducteurs littéraires, ce qui constitue un terrain de recherche à explorer.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".