Contrastive Analysis of Translation Shifts in Lexical Repetition in Arabic-English Legal Translations
Bibliographic record
Abstract
The study investigates some preliminary generalizations regarding the standards regulating the translation types and directions, the lexical repeatability of legal texts shifts. It also compares lexical repetition in Arabic legal texts and that of the corresponding English translation on the basis of lexical repetition type, frequency and distribution. It is a descriptive study that employed parallel corpora to compare lexical repetition in the source language (SL) text with its translation in the target language (TL) text. The research corpus consists of an Arabic legal text and its English translation. The researcher examined the different forms of shifts in the translated text, and the motivation of the translator for utilizing each translation shifts. The result proves that translation shift is an inevitable phenomenon. The various types of translation fell under three categories - avoidance of lexical repetition, retention with alteration, and addition of repetition. In the process of translation from Arabic to English, certain basic concepts cannot be replaced; and as such a translational shift (in lexical repetition) is required to appropriately convey ideas from Arabic to English. Arabic tends to use lexical repetition (LR) more than English, but for the legal texts, English uses LR as well as Arabic. The most common shift detected in this corpus is Partial shift. The results display that the roles of ‘repetition’ are not always preserved, sometimes they can be lost. Multiple translation methods were utilized by the translator. These include deletion, paraphrase, synonym and near-synonym, modulation and pronominalisation.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".