Problems of Translating Legal Contracts: Perspectives of Saudi Translation Students
Bibliographic record
Abstract
Legal translation is one of the challenging domains for translation students. In Saudi Arabia, university translation students are reported to encounter difficulties while translating legal contracts from English to Arabic and vice versa. Also, the literature shows that translation students use certain strategies to overcome these difficulties. This study attempted to examine the most common challenges/difficulties encountered by Saudi translation students when translating legal contracts and the strategies used by them to overcome such difficulties. In order to achieve these goals, the researcher used the descriptive analytical approach and used the questionnaire instrument in order to collect the data from the research sample. The population of this research consisted of all Saudi translation students in two Saudi universities, namely King Saud University and Imam Mohammad Ibn Saud Islamic University. The research population are those students who study at the English language department in each university in the fourth year whose number is (106) students. The target sample is (50%) of the research population. So, the sample size is (53) students, being selected randomly. The findings of the study showed that legal binominal expressions and parallel structure, the structure of legal sentences, the multiple negatives, and the legal text layout are the major challenges that encounter Saudi translation students when translating legal contracts. On the other hand, parallel texts, CAT tools, and Google translation have been reported as strategies used by Saudi translation students to overcome the difficulties they face when they translate legal contracts. The results of the study have important implications for translation teachers, translation syllabus designers, universities, and translation students.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".