Translation of Poetry: A Study of Translatability of Pragmatic and Cultural Elements
Bibliographic record
Abstract
Poetry is a genre that states more in a few words because much of it is cultural content that is assumed to be comprehended by the reader(s). This very characteristic of poetry of being rooted in cultural ethos makes its translation complex and even "untranslatable." Imr-ul-Qais’s Muallaqa is an Arabic classic which has been a symbol of the shared Arab identity, values, and magnanimity. In addition to its unsurpassed poetic brilliance, it is an epitome of the culture of the pre-Islamic Arab world. There are many translations of this masterpiece into English, though each is unique in terms of interpretations, liberties, and constraints. This study examines three prominent translations of Imr-ul-Qais's Muallaqa by Arberry, Johnson, and Mumayiz with reference to Venuti's (1995) dichotomy of domestication and foreignization. The aim is to identify translators’ strategies in tackling the translational challenges, as well as the implications thereof, in order to bridge the linguistic and cultural divide as well as if and what is the nature of the loss of meaning in the process. Results showed that Arberry aims for a poetic rendition in blank verse, focusing on semantic and syntactic fidelity rather than rhyme and meter. Johnson employed transposition and modulation, resulting in a more prosaic translation that lacks the Arabic ethos. Both translators leaned towards domestication, prioritizing English comprehension over retaining the original sentiment. Mumayiz, a native speaker of Arabic, provides a more rhythmic translation, with greater effort to provide English readers with insights into the original text, hence leant more on foreignization than domesticaion.
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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 | 0.019 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".