Définition d’une méthode de recensement et de codage des verbes en langue technique : applications en traduction
Bibliographic record
Abstract
Définition d'une méthode de recensement et de codage des verbes en langue technique: applications en traduction – Le présent article aborde le problème de la description terminographique des unités verbales en langue technique. Le problème est traité sous l'angle de la traduction spécialisée. Le traducteur technique doit reproduire un usage spécialisé dans une langue d'arrivée : les renseignements dont il a besoin pour y parvenir sont souvent difficiles à trouver en ce qui concerne les unités verbales. Nous proposons donc un modèle de description des verbes qui rend compte de leurs multiples acceptions en langue technique. Ce modèle est fondé sur les articles du Collins Cobuild et des travaux antérieurs portant sur la définition des verbes qui tient compte de leur contexte d'utilisation.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".