Problems Encountered in Translating Cultural Expressions from Arabic into English
Bibliographic record
Abstract
This study aimed at investigating the problems that Jordanian graduate students majoring in the English language faced when translating culture–bound expressions. To achieve the goal of this study, the researchers selected a random sample that comprised 60 graduate students who were enrolled in the M.A program in three Jordanian universities during the second semester 2009/2010. The researchers designed a translation test that consists of 20 statements which M.A students were asked to translate from Arabic into English. Each statement contained a culture-bound expression based on Newmark’s categorization of cultural terms. Proverbs, idioms, collocations and metaphors were extracted from different cultural materials, i.e., legal, historical, religious, social... etc. The researchers also conducted informal open-ended interviews with experts in the field of translation to yield additional information from the experts’ point of view regarding these problems, their causes and solutions. The results of the study revealed that graduate students encounter different kinds of problems when translating cultural expressions. These problems are mostly related to: 1) unfamiliarity with cultural expressions 2) failure to achieve the equivalence in the second language, 3) ambiguity of some cultural expressions, 4) lack of knowledge of translation techniques and translation strategies. In light of these results, the researchers recommend narrowing the gap between cultures through adding more courses that deal with cultural differences, cultural knowledge, and cultural awareness, especially in the academic programs that prepare translators.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".