Translating Proper Nouns: A Case Study on English Translation of Hafez’s Lyrics
Bibliographic record
Abstract
Proper nouns are regarded so simple that they might be taken for granted in translation explorations. Some may believe that they should not be translated in transmitting source texts to target texts. But, it is not the case; if one looks at present translations, he will notice that different strategies might be applied for translating proper nouns. They might often be problematic in translation especially in the course of rendition between different cultures. Thus, they are worth exploring. The present study aims to investigate the strategies that have been used in rendering proper nouns by Pazargadi (2003) in his English translation of Hafez’s lyrics. For this purpose, Vermes’ (2003) model of translation strategies for rendering proper nouns has been adapted by the researchers in the study. It has been revealed that the translator has used the transference strategy most preferably for rendering proper nouns of Hafez’s lyrics into English.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".