Translation Strategies for Reference Switching (IltifAT) in SURah Al-Baqarah
Bibliographic record
Abstract
Reference switching (Iltifat) is a unique style found in the holy Qur’an. It is a rhetorical device in Arabic language. Nonetheless, it is used in an extensive and complex manner in Qur’an. Reference switching is defined as the change of speech from one mode to another. However, reference switching constitutes a problem for translators while transferring reference switches from one language to another, particularly in the Qur’anic discourse. This paper looks at the translation of reference switching in surah al-Baqarah specifically focusing on the translation strategies employed by the translator Yusuf Ali to transfer reference switching that is obvious in the Qur’an to English language. Personal reference switching has been classified into five categories: namely the switch from third to first person pronoun; the switch from first to third person pronoun; the switch from third to second person pronoun; the switch from second to third person pronoun and the switch from first to second person pronoun. Newmark’s translation strategies are used as a basis to explore the translation strategies applied by Yusuf Ali. The study reveals that Yusuf Ali opted for five strategies in translating reference switching in surah al-Baqarah. They are literal, expansion, free translation, compensation and a dual strategy of transposition and footnote. However, the study reveals that these translation strategies are inapplicable in conveying the meaning of iltifat. The findings of this study will hopefully pave the way for more investigations into the translatability of reference switching in other surahs of the holy Qur’an. Additionally, the findings of this study will be a step forward toward improving reference switching translation in future interpretations of meanings in 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".