Bibliographic record
Abstract
The article focuses on the problems of social and cultural aspects of the translation process, It has been emphasized that not only linguistic characteristics but social and cultural constituents of the translated text influence its quality. While translating, it is of paramount importance to take into account the extent the culture is involved into the text, and the text is involved into the culture. Language, being a semiotic system, is projecting onto sociocultural and semiotic aspects of translation. The translator should be aware of the culture, customs, traditions, social background expressed both in the source language and the target language as he is presenting to the foreign language audience not only a literary work but also the country of its origin, constructing its image, and the image of its culture In this respect it is important to analyze the role of individuality in translation process. It has been offered to disclose the major stages of a translator’s individuality development process in the creative activity of translating fiction. An American scholar and translator Dr. Michael M. Naydan and f Canadian scholar, translator, and editor Roma Franko have been chosen as a model of a translator in a contemporary translation industry. The choice has been stipulated by a number of reasons: the wide-world recognition of their achievements and their constant striving to popularize the Ukrainian culture in the Anglophone world. The major stages of Michael M. Naydan’s personality as a scholar and as a translator as well as Roma Franko have been considered in the article. Major emphasis is laid on their Ukrainian-English translations which includes prose and poetical works. An attempt has been made to reveal the basic translation tools which they employ to achieve an adequate translation. The article contains the information about the creative activities of Michael Naydan and Roma Franko, offers further perspectives of their study.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".