Translating Contextualized Arabic Euphemisms into English: Socio-Cultural Perspective
Bibliographic record
Abstract
This study examines the role context plays in determining the translation strategies pursued by translators of Arabic euphemisms into English. Due to different cultural backgrounds, adherence to the employment of euphemism in a social context may differ in both Arabic and English. While some situations call for the use of euphemism in one culture, the other culture finds no point in using such euphemisms for such situations; preserving the original Arabic euphemisms when rendered into English in this case could lead to misunderstanding and may deprive the Source Language (SL) from a cultural trait. The study derives evidence from 11 Arabic euphemistic expressions taken from five literary masterpieces written by the Egyptian novelist and Noble Prize winner Najib Mahfouz, and it looks into the English translation of these euphemisms. The present study attempts to advance the proposition that Arabic euphemisms in their context exhibit fluctuating, unstable meaning, which emanates from various contextual factors such as speakers, addressees, shared knowledge and background information, and hence these factors combined dictate on translators the chosen translation strategy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".