Investigating Cultural Competence in English-Arabic Translator Training Programs
Bibliographic record
Abstract
This empirical study investigates the level of translation competence in English-Arabic translation among postgraduate translator trainees in the American University of Sharjah and the University of Sharjah in the United Arab Emirates. It specifically examines the trainees’ competence in rendering from English into Arabic a carefully selected sample of fifteen culture-specific expressions used in contextualized sentences, as well as the trainees’ awareness of the translation procedures employed in their renditions. The results have revealed the informants’ rather low performance in the renditions of culture-bound expressions from English into Arabic; their major types of errors involved incorrect meaning , under-translation and omission . The errors have been mainly attributed to the informants’ inadequate knowledge of English culture, their lack of awareness of the significance of the translation brief while translating, and their inappropriate use of dictionaries. Further, the informants’ improper knowledge of the translation procedures employed in rendering culture-specific expressions has been evidenced. The paper ends by offering some suggestions for developing cultural competence in postgraduate English-Arabic translator training programmes.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".