Culture-Bound Collocations in Bestsellers: A Study of Their Translations from English into Turkish
Bibliographic record
Abstract
This paper discusses the treatment of culture-bound collocations in translations of three recent English bestsellers into Turkish. The findings are categorized as per the nature of the example and translation strategy, and they are further discussed within the framework of domestication and foreignizing. Factors that may affect translators, such as context, the demands of publishers in Turkey and the genre of the novels – bestsellers – and the relations between best-sellerization, popular fiction, and translation are also discussed. The conclusion includes reflections concerning the consistency in the choices of translators, the least preferred strategies and eleven factors that may affect the translators of bestsellers. It is argued that the fact that the source language is English and the source books are bestsellers have affected the choices of the translators. In conclusion, some suggestions in reference to the translation of bestsellers are made and it is emphasized that not only translations of classical literature, but also of popular fiction constitute a fruitful field of study for translation scholars.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".