Bibliographic record
Abstract
Le processus de la traduction n’obéit pas au schéma linéaire d’une séquence de transformations linguistiques présupposant qu’existent d’une langue à l’autre des « axes paraphrastiques » – ainsi qu’aimeraient le penser bien des linguistes et comme s’efforce nécessairement de l’opérationnaliser la traduction automatique (T.A.). Le plus souvent, au contraire, la traduction se caractérise par la discontinuité : le passage du texte-source (To) au texte-cible (Tt) implique un saut ( saltus ). Il se produit donc un processus de déverbalisation entre le texte original qui n’est « déjà plus » là et sa traduction qui n’est « pas encore ». Mais le concept de déverbalisation fait problème. Il paraît évident en effet que le sens ne saurait exister sans un support – dont la nature reste à définir. Toujours est-il qu’en attendant les acquis scientifiques à venir d’une traductologie inductive , relevant des sciences cognitives, il y aura lieu de penser les processus de la traduction dans les termes d’une traductologie productive .
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".