(Re)translating Horace into Ukrainian Modernity: From Mykola Zerov to Andrii Sodomora
Bibliographic record
Abstract
This article focuses on the history of translations and the reasons for translating the Roman classics into Ukrainian in the late twentieth to early twenty-first centuries, as illustrated by the case of Horace. Translation practices, as well as the socio-cultural status and habitus of the translator-classicist, have been varied but have intersected in many respects throughout the twentieth century. This article highlights the major developments in the approach to translating Horace throughout the twentieth century. It mostly focuses on the attitudes and strategies of Mykola Zerov and Andrii Sodomora, who are among the key figures in the twentieth-century theory and practice of translation in Ukraine. The first major development comprises the critical debate regarding translation in the 1920s initiated by Mykola Khvyl'ovyi, whose position was supported by Zerov. The article discusses both the translation practice of Zerov and his reader-oriented theory of verse translation. The second crucial point consists of the revision by Sodomora, starting from the 1980s, of a paraphrasing strategy worked out by Zerov. In his retranslation strategy, applied to his earlier translations from Horace and substantiated in his literary essays, Sodomora exhibits a positive reconsideration of the role and importance of literalist precision in translating the Roman classics, as exemplified by Horace. Sodomora’s evolving approach toward higher precision in translating the classics stems from a close reading of the authentic cultural contexts, structural poetics, philosophical messages, and hidden intertextuality of the source texts. Also, it resonates with Walter Benjamin’s model of literalism, which in many respects appears useful when applied to post-Soviet literary conditions in Ukraine.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".