Translation of Onomatopoeia: Somewhere between Equivalence and Function
Bibliographic record
Abstract
English-Persian translation of novels deals with the challenges of understanding and transferring different linguistic aspects such as those of onomatopoeias. These elements are expected to create difficulties for translators as they are realized differently in English and Persian. Although some studies have been done to identify onomatopoeias in different languages, they are less debated in the area of translation. This study concentrates on English translation of 125 onomatopoeias in the novel A Tale of Two cities written by Charles Dickens and their Persian translations done by two translators. It aims at identifying English onomatopoeias in the corpus and the translation techniques used for translating them by the two translators. Furthermore, taking prospective approach, it comparatively assesses the two translated versions in terms of their success of translation of onomatopoeias from English into Persian. Finally this study aims at proposing a guideline which helps the translators to translate onomatopoeias in English Novels into Persian.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".