Word-Formation Characteristics of Anglicisms in the Russian Slang
Bibliographic record
Abstract
The paper deals with characteristics of Anglicisms that operate in the contemporary Russian slang with the emphasis on their word-formation features. The penetration of these lexical units into the Russian language provides a researcher with a linguistically interesting material as the English and Russian languages represent typologically different language systems. To begin with, the research focuses on the origin of the individual word bases and affixes from which the Anglicism instances analysed by us are formed. Moreover, we treat the representation of non-motivated and word-motivated lexemes. At the same time, word-formation means, methods and procedures for the formation of Anglicisms in the Russian slang are taken into account. The above-mentioned lexical units find their word-formation realization within these processes: derivation, composition, compounding, clipping, acronyms, blending, conversion, calques, fusion, univerbization and phonetic mimicry. Additionally, they display differences in onomasiological categories across the studied field. With respect to this, we cover the word-formation features of the processes: word-formation strings, paradigms and nests; word-formation types, onomasiological categories and types of onomasiological categories. The practical utilization of the research is possible in the areas of comparative and applied linguistics and translatology when searching for equivalents of lexical units in typologically different languages. Furthermore, the results of the research are applicable in the methodology of teaching foreign languages.
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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 | 0.075 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".