Fidelity in Rendering the Quranic Arabic Homonymous Words Sawai into English in Light of Skopos Theory
Bibliographic record
Abstract
This analytical study aims to identify discrepancies in the fidelity of translating the Qurʾānic Arabic homonymous words sawāi سَوَاءِ into English. This study adopts a qualitative approach with a qualitative design, as it hinges on perspectives and reviews rather than quantitative evidence. Also, the study shows that fidelity in translation can be understood through the lens of Skopos theory, which is utilized in this study as a frame of reference. Furthermore, the findings reveal that some translators occasionally rely on interpretive translations and succeed in capturing the intended meaning of the Qurʾānic Arabic homonymous words sawāi سَوَاءِ into English in the schemes they are targeting. In other cases, however, they lean on literal translations, often aiming to convey the connotative meaning of these Qurʾānic Arabic homonymous words. Several challenges and losses were found in the three English translations explored: fidelity in translation occupies a role in gauging the fidelity of a rendered text. The study also indicates that the translation of the Holy Qur’an in general, and of the Qurʾānic Arabic homonymous words sawāi سَوَاءِ in particular, should be led by its Skopos rather than unrealistically focusing on achieving fidelity.
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 | 0.043 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".