Slang Vocabulary of the Ukrainian and English Languages: Translation Aspect
Bibliographic record
Abstract
The modern language of young people is characterized by expressiveness and special coloring, widely represented by slang, which is the most expressive form of vocabulary in linguistics. That is why the question of using slangisms of modern society is quite relevant. The purpose of this research work is to study the term slang, determine its place in the system of Ukrainian and English language lexicon, study the nuances of the formation and functioning of slang, as well as determine the peculiarities of translation of English youth slang into Ukrainian language. To study the issue of Ukrainian and English slang vocabulary through the prism of translation aspect, the method of theoretical analysis, linguistic analysis, transformational analysis, as well as the descriptive method of research were used. Using the method of theoretical analysis, the basic theoretical concepts, in particular slang, were studied, and the scientific literature devoted to the topic of research was analyzed. Using the research’s linguistic analysis method, the pragmatic aspect of Ukrainian and English slang was studied. The transformational analysis method helped study the use of translation transformations in the translation of English slangisms. Using the descriptive method, the peculiarities of slang usage in Ukrainian and English were depicted. As a result of the scientific research, the tendencies in the development of modern slang vocabulary of Ukrainian and English languages were studied and analyzed, as well as the changes in the potential in the translation aspect of slangisms, which is the prospect of further scientific research in this direction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".