The Specifics of Translating Poetry. The Study of the Specifics is Based on the Material of the English and French Languages
Bibliographic record
Abstract
The work of René Char remains poorly studied in Ukrainian literary criticism, and there are few translations published. In this paper, attempts were made to translate some of René Char's poems from the poetry collection "Fureur et mystère" (Rage and Mystery), which is central to his work. The analysis points out both the advantages and disadvantages of the translators' work. The intertextual connection between the poems "Allégeance" and "Allégement" is revealed and its importance for the interpretation of both texts is shown. This overlap was not shown in the translation. Ways were found to convey this connection within the poem itself, but the option of conveying it in the title was suggested. Some general difficulties that may arise during translation are identified, related to the transmission of rhythm, meter, graphics of the poem, syntax, as well as the figurative component of René Char's poetry. It has been established that the hermeticity of his poems is absolute: interpretation requires knowledge of the historical, cultural, and biographical contexts, as well as an in-depth familiarity with other poems by Char. However, the latter condition cannot be fulfilled by foreign-language readers. As we have discussed above, his works lack translations. So far, no translation of the entire book of poems has been made, and translators (including us) are working on translations selectively. Thus, in the course of our work, we discovered problems related to the translation of René Char's poems. In our translations, we tried to convey the original text with maximum accuracy, although this was not always possible. Considering the difficulties reflected in our comments on the translations, translations of other poems may be performed.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".