Translation Strategies Utilized in Rendering Social Etiquette in Holy Quran
Bibliographic record
Abstract
The study aims at filling the gap in the translation of Quranic verses concerning social etiquette[1]. Translating culture specific items (CSIs) can be challenging because certain elements have meanings particular to the culture and the language in which they appear. These meanings do not exist necessarily in other cultures. Translation strategies tend to solve translational problems by applying specific procedures to the translated text. The article at hand has studied the translation strategies used by seven translations of the Holy Quran relating to social etiquette, based on the selection of Quranic verses pertaining to social etiquette as followed by practicing Muslims through analyzing nine English translations from 1930 to 2009. It is found that the dominant translation strategy is the literal translation, with 89% of all strategies in preference to other strategies such as free translation, neutralization, paraphrasing, lexical creation, and adaptation.[1] Eittquette is defined as “a set of customs and rules for polite behaviour, especially among a particular class of people” Collins (2022).
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".