Les premières traductions de l’<i>Iphigénie à Aulis</i> d’Euripide, d’Érasme à Thomas Sébillet
Bibliographic record
Abstract
En 1506, Érasme est le premier à traduire en latin des tragédies grecques entières, en l’occurrence deux tragédies d’Euripide, Hécube et Iphigénie à Aulis. S’il adopte pour l’Hécube une traduction vers à vers, il opte dans l’Iphigénie pour une traduction plus détaillée en veillant à produire dans la langue cible les effets de l’original. Dans son ouvrage sur L’Hécube d’Euripide en France, Bruno Garnier a montré comment la traduction latine d’Érasme a influencé la première traduction française de l’Hécube, attribuée à Guillaume Bochetel (1544). Cet article est consacré aux premières traductions de l’Iphigénie à Aulis et, en particulier, à celle de Thomas Sébillet qui se mesure à Érasme pour démontrer, contre Joachim Du Bellay, la capacité d’une traduction poétique à illustrer la langue française. In 1506, Erasmus was the first person to translate complete Greek tragedies into Latin, in this case two tragedies by Euripides, Hecuba and Iphigenia at Aulis. Though he used a verse by verse translation for Hecuba, he opted in Iphigenia for a more detailed translation, taking care to reproduce in the target language the effects of the original. In his work on Euripides’ Hecuba in France, Bruno Garnier has shown how the Latin translation of Erasmus influenced the first French translation of Hecuba, attributed to Guillaume Bochetel (1544). This article addresses the first translations of Iphigenia at Aulis and in particular that of Thomas Sébillet. He pitted himself against Erasmus to demonstrate, contrary to Joachim Du Bellay, the capacity of a poetic translation to exemplify the French language.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".