Features of Translating Scientific Texts into English
Bibliographic record
Abstract
The theoretical significance lies in the fact that the article makes a certain contribution to the development of theoretical and practical aspects of translating humanitarian texts in the scientific style. The research aimed to address the following specific tasks, namely, to consider ways to achieve equivalence and adequacy in the translation of scientific texts in the humanities. An equally important task was to show and analyze translation transformations, in particular the methods of translating metonymies, abbreviations, metaphors, and phraseological units. It was noted that the difficulty in translation was caused by non-equivalent words, terms, and phrases, for which it was necessary to find a counterpart in the target language or at least to convey the meaning of the word so that the foreign reader could understand what was being said. The exact genre of the text was explained by the fact that, at first glance, the text is not a scientific one. It is noteworthy, that firstly, it is a text in the field of foreign language teaching, and secondly, it contains stylistic techniques, metaphors, metonymies, and phraseological units that are not typical of the scientific style of the Ukrainian language. In English, this is permitted. There is a problem with choosing the most appropriate translation technique for conveying the stylistic, lexical, and grammatical features of the text. The analysis of translation in the humanities has led to the following conclusions. Professional translation requires a good knowledge of the field of activity in which the translation is to be performed. Equally important is the knowledge of the source language and the ability to express one's thoughts competently in the target language. Moreover, when translating humanitarian texts, adequate, descriptive, metonymic, metaphorical, antonymic, and substitutional translations are often used. Accuracy, brevity, conciseness, clarity, literary quality, and equivalence are the main requirements for translating scientific humanitarian texts.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".