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Record W4387818278 · doi:10.5430/wjel.v13n8p558

New Slang Expressions-Neologisms to Denote the Phenomena of War: A Translation Aspect of the Neglect

2023· article· en· W4387818278 on OpenAlexvenueno aff
Vitalina Tarasova, Світлана Романчук, Tetiana Kapitan, Інна Демешко, Tetiana Leleka

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsNeologismSlangLinguisticsIdeologySociologyComputer sciencePoliticsPhilosophyPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0040.021
Scholarly communication0.0070.012
Open science0.0010.005
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.024
GPT teacher head0.242
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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