Bibliographic record
Abstract
Tartarin chez les Anglos : intraduisibilité et pertinence – Cet article applique les concepts de la théorie de la pertinence de Sperber et Wilson à la comparaison de cinq traductions anglaises et américaines de Tartarin de Tarascon d'Alphonse Daudet. Pour que deux messages ou textes se ressemblent, il faut, selon cette théorie, qu'ils comportent des explicatures et implicatures semblables. De plus, la compréhension ne doit exiger qu'un effort minime d'assimilation. Nous avons utilisé ces concepts pour analyser les différentes solutions apportées par les traducteurs comme équivalents des méridionalismes et des éléments provençaux que contient le texte de Daudet. Certaines traductions américaines conservent tels quels un nombre relativement élevé de termes étrangers et la théorie de la pertinence appliquée à la traduction permet de voir que ce maintien comporte des avantages.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".