Cultural and Sociolinguistic Issues in English-Arabic Translation of Collocations
Bibliographic record
Abstract
Collocations are words that must accompany each other. When it comes to translation, collocations usually pose problematic cultural and sociolinguistic issues. This paper attempts to shed light on some of these problems that participants of this study faced in the English-Arabic translation of collocations. In order to examine these issues, a questionnaire was given out to 40 MA students majoring in English Language at The Hashemite University, Yarmouk University, and Al-al-Bayt University: 15 males and 25 females. The results showed that translators faced these cultural and sociolinguistic problematic issues: The word order within the same collocation, the availability of acceptable equivalents in the target language (TL), and linguistic issues related to religious words. The results also revealed that participants had not enough proficiency in collocations. Data were tabulated and analyzed. In addition, this study concludes with some recommendations, including offering at least two courses related to English and Arabic collocations in order to solve the problematic issues in translating collocations in Jordanian universities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".