Subtitling Idiomatic Expressions from English into Arabic: Enola Holmes as a Case Study
Bibliographic record
Abstract
Translation involves conveying a text's pragmatic, cultural, and semantic components to a different language. On the other hand, idiomatic expressions pose a challenge to translators because they are culturally distinctive and incorporate various cultural nuances. This study employs a qualitative case study approach to identify the most frequently used subtitling strategy for translating idioms in the movie Enola Holmes (2020). Additionally, it aims to examine the influence of various types of idioms on the subtitler’s selection of subtitling strategies. Pedersen’s taxonomy for rendering extra-linguistic cultural references in subtitling was selected for data analysis as it was expressly created for audiovisual translation. The results show that substitution was the most frequently used translation strategy for subtitling idioms, particularly in subtitles that were remarkably pure and semi-idiomatic. The second most dominant strategy was the official equivalent, which was applied to render all three types of idioms. Direct translation was the third most used strategy, especially in subtitling literal idioms. Further research could investigate how the strategy used to translate idiomatic expressions affects the audience’s overall interpretation of the implicit meaning of colloquial expressions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".