History and Policy of Translating Poetry: Azerbaijan and Its Neighbors
Bibliographic record
Abstract
Throughout the history of civilization the art of translation has existed as a bridge that connects different cultures. The article focuses on the history of poetic (and other) translations in the Middle East and territories abutting Azerbaijan from the Middle Ages to the 20th century. It also explores the practice of translating holy books and its influence on the region, as well as the tradition of nezire, or writing a new book under the inspiration of an original one rather than simply translating the original. The second part of the article discusses the history of poetic translation into and from the Azerbaijani language, especially translation work from Abbas Sehhet and Samad Vurghun, two renowned translators in Azerbaijani history. Finally, important aspects of the art of translating poetry are reviewed and analyzed, such as poetic forms and metaphors, rhythm and rhyme schemes, and the style of the text. The article concludes by making the point that poetry should indeed be translated; however, translators must take many factors into account in their work so that the target text reflects as much as possible the beauty of the original.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".