Some Questions of Linguocultural Specificity Communication at the English Humour Translation
Bibliographic record
Abstract
This problem is relevant today because it is necessary to study the issues of the correlation of language, culture and translation, so far as translation is a link between linguistic cultures speakers. Obvious lack of researches in the field of linguocultural specificity affects the quality of the translation and the adequacy of reflection of ethno-linguistic worldview in the minds of other languages speakers. The objective of the article is to identify the degrees of interaction of language and culture in the translation process to provide a deep penetration in the national associated meanings of original literary works. Leading approach to the study of this problem is the analysis that was carried out on basis of descriptive, comparative methods, on the method of a literary text description, involving elements of linguistic and cultural analysis. Methodological basis became researches in linguistics, intercultural communication and translation studies. The paper revealed that linguocultural humour study suggests the priority coverage of the values that are relevant for the compared cultures. These values may get different expression in humorous texts. English humour includes relevant characteristics of universal humour and humour of those social groups that make up the English nation. The article materials may be useful for further research in this area, for effective translation techniques development.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".