Translation of Islamic Arabic Poetry: A Two-Stance Methodological Framework
Bibliographic record
Abstract
This study investigates whether it is possible to translate Islamic Arabic poetry, as a universal literary religious genre, into English to a high level of quality. A global-local translation framework (Halimah, 2020) is applied to the translation of Imam Ashshafi’ee’s[i] poem “الرضا بقضاء الله”/ “Accepting Fate with Pleasure” into English, where a methodological collaboration between a bilingual translator and an English poet manifests a two-stance methodological framework. The results, along with the qualitative and quantitative analysis of poetic extracts used in this paper, indicate that this framework has resulted in improving the quality of Islamic Arabic poetry translation. The translated materials proved to be ‘novel and appropriate’, achieving a very high 90% of approximation of the original text against ACNCS criteria. This framework can also be applied by translators of other literary and non-literary texts to bring more structure and clarity to the discipline of translation.[i]Muhammad bin Idrees Ashshafi’ee (DoB:150H/767A.D died in 204H/820A.D.) The founder of the Sunni School of Law مذهب الشافعية
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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 | 0.110 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".