The Formalized Semantics of Neologisms-Slangisms in the Context of the English Translation of A Military Narrative
Bibliographic record
Abstract
Each new designation associated with a designation already fixed in the language is traditionally and automatically considered a case of semantic neology and is a case of polysemy. This simplistic analysis does not take into account morphological analysis and ignores the semantic laws that govern changes in meaning. The paper examines the meaning and evolution of semantic neologisms in military English discourse. In particular, three types of neologisms can be distinguished: neologisms-borrowings, neologisms-homonyms, and semantic neologisms. The emphasis is placed on semantic neologisms since they are the ones that become a problem for translators due to the tendency to polysemy. The paper examines the neologisms of military discourse in the context of the specifics of the narrative of translations of military texts by S. Zhadan. Unlike a large number of other linguistic concepts, such as a phoneme, morpheme, the concept of neologism seems to be well known to everyone, and in general, consensus means “new word”. Based on this, one might expect unanimity in the identification and analysis of neologisms and slangisms. However, there are many discussions about the neological status of a particular lexical item. Scientists emphasize a very high rate of dispersion of linguistic new formations, without a clear differentiation between neologisms and slangisms. Therefore, in the presented work we analyse the problems of translation of semantic neologisms and slangisms on the basis of English translations of the existential specificity of the narrative of the war-themed texts by S. Zhadan “Boarding School”. The issue of the newest intuitive concept of neology and the potential of slangisms to form a formal matrix, and neologisms - a semantic one, is actualized.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".