Color Idiomatic Expressions in the Translation of Naguib Mahfouz’s Novel “The Thief and the Dogs”: A Case Study
Bibliographic record
Abstract
Colors play a vital role in people’s communication. They do not only express the colors themselves, but are also endowed with cultural characteristics of each nation. In other words, colors in different languages and cultures may convey different associative meaning and people from different cultures react to colors in different ways. The aim of this study is to investigate the translation of color idiomatic expressions from Arabic into English in Naguib Mahfouz’s novel “The Thief and the Dogs” and to what extent is color idiomatic expressions retained, wasted and distorted. Moreover, the study aims at exploring the different translation strategies applied in translating color idiomatic expressions in this novel and finding out the similarities and differences between their meaning in both languages. The study mainly focuses on the most common colors: black, white, yellow, red, green and blue. For the purpose of the study, a number of Arabic idiomatic expressions along with their equivalents in English were gathered from the novel and were contrastively studied side-by-side with their translations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".