Investigating Saudi Student Translators’ Difficulties and Strategies in Translating English Culture-bound and Idiomatic Expressions: A Quantitative Study
Bibliographic record
Abstract
Given the scarcity of studies looking into EFL student translators’ difficulties and strategies revealed in tasks involving translation of English idioms, this study inspected both the translation problems and strategies reported by Saudi university students majoring in English translation when translating English culture-bound expressions and idioms into Arabic. To achieve this aim, the researchers recruited a random sample of 90 Saudi female students to complete a 30-item translation test that required written translation, followed by a short self-devised questionnaire. Validity and reliability for both study instruments were established by test-piloting and translation verification checks. The various translation strategies used by the participants were categorized according to existing strategic processing frameworks developed by Baker (1992) and Newmark (1998). This study revealed how the process of translating English idioms posed some difficulties for most of participating student translators. In particular, two main translation problems were observed: unsuccessful attempts to achieve item equivalence into Arabic, and inadequate knowledge of strategic translation. Moreover, the study reported a variety of five main translation strategies (paraphrasing, partial equivalence, omissions, use of precise English expressions, and total equivalence) being used in the test. Drawing on the study findings, some pedagogical implications and recommendations were presented and discussed.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".