Strategies and Procedures Used in Translating Ideological Islamic-Related Texts from English into Arabic
Bibliographic record
Abstract
This study aims to identify the various strategies and procedures that translators use in rendering ideological Islamic-related texts from English into Arabic. To achieve this purpose, the researchers have designed a translation test consisting of 10 extracts with ideological content written by Muslim and non-Muslim writers. The researchers have selected a purposive sample of 20 translators to perform the test. Only 16 of them have responded. The results of the test have been analyzed qualitatively and quantitively. The study reveals that two strategies have been used by the translators: foreignizing and domesticating. It also reveals that 12 procedures have been employed by the translators: recognized translation, literal translation, naturalization and paraphrasing procedures underlying the foreignizing strategy and transposition, equivalence, omission, addition, glossing, magnifying, moderating and the label procedures underlying the domesticating strategy.
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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 | 0.016 |
| 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 | 0.001 | 0.001 |
| 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".