Translating the Islamic Religious Expressions in Taha Hussein’s Novel ‘Al Ayaam’ by E. H. Paxton
Bibliographic record
Abstract
Translating religious expressions from Arabic into English seems problematic where the cultural backgrounds must be known for the translator to choose the appropriate equivalence and to help bridging the cultural gap between two cultures. As a result, this paper investigates to what extent have Venuti’s strategies (domestication and foreignization) been successfully implemented in rendering the religious expressions in Taha Hussein’s The Days (الأيام alʾayām) by E. H. Paxton in the translating process. It also asserts the challenges involved in translating the Islamic religious expressions in this novel. Consequently, this paper has randomly chosen, discussed, and compared 10 Islamic expressions of Hussein’s novel with their English equivalents. In addition, two Arabic raters were consulted. Venuti’s (2004) domestication and foreignization method was used to analyse these examples. The study concludes that the translator uses the two methods in rendering the religious expressions. The foreignization strategy was used more than domestication. Six examples have been foreignized; whereas, four of them were domesticated. The study also reveals that the use of both strategies supplements and supports the translation accuracy. Finally, the different cultural backgrounds, religions, expressions, costumes, traditions have to be the ultimate concerns of the translators in translating the Arabic religious expressions into English language.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".