SOME PECULIARITIES OF S. LEACOCK’S SHORT-STORY «THE MAN IN ASBESTOS: AN ALLEGORY OF THE FUTURE» TRANSLATION INTO UKRAINIAN
Bibliographic record
Abstract
The role of S. Leacock as a representative of English-Canadian literature and peculiarities of his creative works are given in the article. The peculiarities of the literary translation which aim is to reflect ideas, feelings transforming the author’s images with the help of another language material, the main features that make it different from a classical one were stated. The scholars who scrutinize the problems of a literary text translation in the contemporary linguistics was found out. The differences between the original text of S. Leacock’s short-story «The Man in Asbestos: an Allegory of the Future» and the text of translation and its translation by A. Yevsa were analyzed in the article. The translation can be called adequate as some change of content of the original text by the target language means did not impact into general perception of the short-story in its translation. The translator conveys the author’s ideas provoking reader’s reaction to the story. A. Yevsa preserved its content, the system of images and the author’s style, emotional atmosphere and plot identity of the original text and the choice of linguo-stylistic devices used in the original text. General peculiarities of the translation into Ukrainian, main grammar and lexical transformations used by A. Yevsa were marked, among which are generalization, concretization, compensation, semantic development and combination of sentences prevail.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".