Traduire Pouchkine en France et au Japon au XXe siècle
Bibliographic record
Abstract
Divided into six chapters, our thesis examines the translation of Evgenyi Onegin, a novel in verse by Aleksandr Pushkin, in France and Japan in the 20th century. In Chapter 1, we introduce our methodological approach, including the eight elements of our translation analysis: the by-who, the who, the what, the for-who, the when, the why, the where, and the how. In Chapter 2, after a brief biography of the Russian poet, we examine his central work Evgenyi Onegin, and its unique structuro-phono-semantic synthesis. The first French mention of Pushkin was in the 19th century, and the 'transfer-related discourse' of that period is the focus of Chapter 3, particularly its creation of the myth of the poet's untranslatability, which would influence translators of the Pushkinian novel into the 20 th century. In Chapter 4, we examine the 11 French translations produced between 1902 and 1996. Because the Japanese discovery of foreign literature---and Pushkin---was the product of political changes during the Meiji period (1868-1912), it is paramount that we examine the pivotal role of 19 th century ideological discourse in which translation is viewed as a means and a condition for the country's modernization. Finally, in Chapter 6, we turn our attention to the 8 Japanese translations of the Pushkinian work produced between 1921 and 1996. Our aim is to demonstrate how the spatio-temporal change influenced the view of translation in general, and translations of Pushkin in particular.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".