Application of Eugene Nida’s theory of translation to the English translation of surah Ash-Shams
Bibliographic record
Abstract
The present study aimed to test the applicability of Nida’s theory of translation to an English translation of surah Ash-Shams of the Holy Qur’an. Towards that general aim, the study provided an overview of Nida’s theory of translation and the aspects whose applicability to the English translation of surah Ash-Shams would be tested. In addition, the study examined the Editor’s Preface to the English translation of the Holy Qur’an from which surah Ash-Shams was selected. A contrastive analysis was also devised and provided to help match the source text with the target text and measure the applicability of Nida’s theory of translation to both texts. The study revealed that, in general, Nida’s theory was applicable with the exception of one aspect related to word order. It is, therefore, recommended that large-scale research be conducted on the applicability of Nida’s theory to an English translation of the whole Holy Qur’an to either confirm the findings of this study or challenge them.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".