Felix culpa : congruence et neutralité dans la traduction des textes de réalité
Bibliographic record
Abstract
Quelle attitude avoir, dans la vie professionnelle et dans l’enseignement, face aux erreurs susceptibles de se glisser dans les textes originaux ? La réponse permet de distinguer clairement traduction littéraire et traduction pragmatique. Dans le second cas, on traduit non seulement un texte pourvu de sens, mais aussi le monde réel dont ce dernier est censé procéder. La traduction du sens cède alors le pas à celle de l’intention, dont la restitution suppose d’adopter une posture à l’intérieur d’une chaîne de communication : le traducteur commet une faute en n’osant pas devenir un interlocuteur. Rechercher la cohérence à tout prix peut toutefois l’amener à tempérer les aspects incongrus du texte original, au risque de faire disparaître des éléments d’information décisifs.
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 | 0.002 | 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 | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".