Strategies Used in Arabic-English Translation of Idioms in Samiha Krais’s Novel Al Qurmiya
Bibliographic record
Abstract
This article aims at exploring the translation strategies adopted in translating from Arabic into English the idiomatic expressions found in Al Qurmiya, a historical novel by the Jordanian writer Samiha Krais (1998/2011). Fifty-five idioms in the Arabic text have been selected and compared with their counterparts in the English text with the aim of finding out how they have been translated. Applying Mona Baker’s (2018) idioms translation model, the researcher examines the translation strategies employed in the translated version The Tree Stump by Nesreen Akhtarkhavari (2019) and analyzes the extent of their effective transference of the meanings of the selected idioms from the source language into the target language. Results show that the strategies applied in the translation are similar to those in Baker’s model and that paraphrasing is the most frequently used strategy followed by the strategy of “using an idiom of similar meaning but dissimilar form”.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".