PROBLEMS OF SUBTITLE TRANSLATION FROM FRENCH INTO UKRAINIAN.
Bibliographic record
Abstract
The article is devoted to the study of peculiarities of subtitle translation from French into Ukrainian and is focused on finding the translation methods that are used in subtitle film translation. Special attention is paid to the possibility of using lexical and grammatical transformations and methods of transmission of cultural peculiarities and realities of Canada. The conclusions prove the fact that cultural peculiarities of Canada and its language have to be respected in order to conserve the pragmatic potential of expression and reach the pragmatic effect. It was proved on the given examples that the adaptation of feature film requires an excellent knowledge of native language. It was revealed the necessity of transmission of foreign culture features that are expressed in specific humour, quibbles, colloquial language and that represent a reflection of producer’s and scenarist’s ideas. In order to economize a place, the most important method in subtitle translation is the omission of the elements that are not obligatory within certain communicative situations for understanding the contextual meaning. It was shown how French dialogues can be transmitted in Ukrainian subtitles using lexical and grammatical transformations.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".