Exploring Stylistic Problems Encountered in Translating Qur’anic Aphoristic Expressions in Surahs Al-Baqarah and Al-Imran to English
Bibliographic record
Abstract
The stylistic approach in the domain of the Qur’anic research is a relatively forgotten field. To fill this research gap, the current study focuses on the stylistic issues encountered when translating Qur'anic aphoristic expressions into English, as well as gaining a deeper understanding of the styles of rendered versions of these expressions which play a significant role in language as a share of acquiring cultural understanding, figurative meaning, expression power and communicative-pragmatic component. To carry out the study, five verses (ayahs) that include this phenomenon were chosen from Chapter 2 Al-Baqarah (“The Cow”) and Chapter 3 Al Imran (“The Family of Imran”) of the Qur’an. Moreover, a descriptive qualitative method was employed in this study. The study revealed that there were numerous stylistic problems and meaning losses and gains in the intended translation in the translations of Muhammad Taqi-ud-Din Al-Hilali and Muhammad Muhsin Khan(1996); Muhammad A.S. Abdel Haleem (2004), and Muhammad Marmaduke Pickthall (1930). The study also found that the translators utilized several different translation strategies such as faithful renditions, free translation, addition, dynamic equivalence, and formal equivalence. Finally, this study offers a more useful definition of aphorism in the translation of the Holy Qur’an.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".