Using Expansion Strategies in Making Untranslatable Areas of Poetry Translatable: Sa’di’s Bustan as a Case in Point
Bibliographic record
Abstract
The purpose of this study was to explore poetry translatability and seek to see what the translators do to compensate those untranslatable areas of poetry. In doing so, the researchers chose a literary work, i.e., Bustan, by one of the well-known Iranian poets, that is, Sa’di (Wickens, 1990) and one of its translations, “The Bustan by Shaikh Muslihu-D-Din Sa’di Shirazi,” by Clarke (1985). They analyzed one hundred verses of his poetry, which were chosen randomly, to see what the translator did to overcome the untranslatable parts of that poetry, i.e., Sa’di’s Bustan. The study showed that the translator used expansion wherein he made some semantic and structural adjustments in his translation so as to make those untranslatable parts translatable and make the translation natural, intelligible and understandable in the receptor language. As a whole, in the selected corpus of the study, ninety two cases of expansion were identified.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".