Near-Synonyms Within the Same Qur’anic Verse: A Contrastive English-Arabic Lexical Analysis
Bibliographic record
Abstract
The Holy Qur’an has features that are difficult for translators to transfer into another language, and they face problems in conveying the different shades of meaning of the verses. From a wide variety of these problems is synonymy. Synonymy refers to words that mean the same or show semantic resemblance to one another. This study examines the translation of two root-sharing synonymous Arabic words, namely استطاع and اسطاع in five well-known English translations. These include Pickthall (1930), Ali (1982), Arberry (1996), Abdel Haleem (2004), and Al-Hilali and Khan (2018). These translations are selected because they are popular in the Muslim World in addition to the fact that the translators belong to different linguistic, religious and cultural backgrounds. The analysis shows that the translators were inconsistent in their selections of the English equivalents for the words under study. Furthermore, they did not convey the slight differences between the words and translated them similarly and interchangeably. The study concludes that some Qur’anic words are untranslatable, and cannot be rendered into another language, and therefore, translators are recommended to include explanatory notes between brackets or as footnotes in order to acknowledge the non-Arab readers that repetition of the words was not haphazard but intended for specific purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".