New Slang Expressions-Neologisms to Denote the Phenomena of War: A Translation Aspect of the Neglect
Bibliographic record
Abstract
The phenomenon of war broadcasts military discourse, which is often considered a “separate language” full of stylistic figures: rhetoric, puns, metaphors, and neologisms. The latter often function as keys to ideology and thus reflect a certain vision of the world. Such categories pose significant challenges for translation, as these “ideological keys” are often incomprehensible to the target audience. First of all, these are terms with connotations that refer to socio-cultural reality, socio-cultural reality, or derived from connotations known only to the military, which are difficult to translate and, according to some scholars, are untranslatable. The purpose of the article is to propose a theoretical model and a methodological description of the ways of translating slang expressions-neologisms of military discourse that have entered the English language as a result of the war in Ukraine. The result of the work is an analysis of the ways in which Ukrainian slang expressions-neologisms entered the English language and the ways in which they can be translated. Each neologism characterizes a certain category according to the criterion of the channel through which it spreads - through peer-to-peer networks and through the media, respectively. The application of the theory of innovation diffusion to neologism provides food for thought about the typology of speakers in terms of their sensitivity and adaptability to neologisms. The difference between disparaging slang expressions-neologisms, in particular, dysphemisms and ethnic slurs and neologisms with ironic connotations is noted. The conclusions of the study formulated the ways of translating neologisms: borrowing, calquing, literal translation, transposition, modulation, equivalence, adaptation, and translation methods: a combination of calquing/literal translation with explanation or definition, deletion and replacement, and translation by equivalent with the creation of own neologism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".