Linguistic Obstacles Faced in Translating Some Unique Qur’anic Cultural Lexical Items into English: Reexploring Some Translation Approaches
Bibliographic record
Abstract
This research paper aimed to identify the linguistic obstacles met in translating some unique Qur’anic cultural lexical items into English through the lens of translation approaches. The study used a qualitative descriptive method and Nord’s (1991) text analysis model in translation. The findings showed that the most used approach to translating the implication of Qur’anic cultural lexical items was that of word-for-word and verbatim translation, and this resulted in a deep meaning loss and manipulating the perfect translation. The study also found that Al-Hilali and Khan’s and Abdel-Haleem’s translation approach, contrastingly, seems to be predominantly translated text-oriented, and thus conforms to the strategy of free translation, putting pivotal descriptive details in brackets, footnotes, or as a paraphrasis. The study concluded that Pickthall was inclined to resort to a literal translation strategy, which often gives rise to obscurity and problems because it does not consider the idiomatic meaning and implicit meaning of the Qur’anic Cultural Lexical Items.
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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 | 0.036 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".