A Study of Persian Translations of English Phrasal Verbs in Harry Potter and the Order of the Phoenix
Bibliographic record
Abstract
English phrasal verbs have special semantic and structural features which make their translation into other languages a difficult task. Yet it seems that these linguistic constructions have been neglected by Persian translation theorists and translators. In this study, the first volume – chapters 1 to 12 – of three of the Persian translations of the novel “Harry Potter and the Order of the Phoenix” by Rowling (2005) were examined along the original text on the basis of Newmark (1988) and Vinay and Darblenet’s (1995) taxonomies of translation procedures. Through juxtaposition of the English phrasal verbs with their Persian equivalents, the type and the frequency of the applied translation procedures were identified and calculated. The researchers used three criteria of accuracy, clarity and naturalness by Larson (1998) to assess the quality of the applied translation procedures in particular and the translations in general. The results of the study showed that equivalence is the most frequent as well as the most successful translation procedure used in the Persian translations. According to the mean of the scores received for accuracy, clarity and naturalness of the translation procedures, it was also found that the translation by Eslamie (2008) fared better in translating English phrasal verbs 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".