Notions essentielles et enseignement de la traduction scientifique et technique
Bibliographic record
Abstract
Le traducteur appelé à traduire dans un domaine scientifique ou technique doit se familiariser avec les notions et la terminologie du domaine. Il doit le faire autant de fois que le nombre de domaines dans lesquels il veut traduire. Chaque fois, c’est comme s’il part de zéro. Cela lui prend énormément de temps, car les concepts sont nombreux. Les études faites en science cognitive et en terminologie ont permis de catégoriser les concepts, de dégager leurs propriétés et d’établir des critères qui permettent de rapidement analyser et comprendre les concepts. Dans le présent article, nous présentons les types et les caractéristiques des concepts ainsi que les questions fondamentales à se poser pour vite saisir les notions essentielles d’un domaine et pouvoir traduire dans ce domaine.
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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".