Linguistic Accuracy in Translating the Qur’anic Arabic Homograph al-Hawā (الهوى) into English: An Equivalence Theory Approach
Bibliographic record
Abstract
Attaining linguistic accuracy in the translation of Qur’ānic Arabic homographs, especially the lexeme al-Hawā (الهوى), into English has created a daunting challenge for linguists, interpreters, philologists, translators, and Qur’ānic scholars. This intricacy is due to the double meaning associated with these lexical items and the complicated exegetic and contextual nuances inherent in Qur’ānic discourse in terms of the general and specific meanings of Qur’ānic homographic lexemes. This analytical analysis aims to investigate the linguistic accuracy of translating the Qur’ānic homograph al-Hawā (الهوى). This study is conducted under the framework of the equivalence theory approach. Utilizing a qualitative, analytical methodology, it is based primarily on analytical frameworks and a comprehensive literature review, favoring qualitative over quantitative methods. The findings imply that Abdel Haleem, Pickthall, and Al-Hilali and Khan predominantly utilized a blend of dynamic, free, contextual, and literal translation strategies to capture the nuanced semantic dimensions of the Qur’anic Arabic homograph al-Hawā (الهوى). However, instances remain where strict adherence to literal translation leads to a failure to aptly express the connotation and implication of these Qur’ānic homographs.
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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 | 0.049 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".