Recognition of English and German Borrowings in the Russian Language (Based on Lexical Borrowings in the Field of Economics)
Bibliographic record
Abstract
This article is the result of a study of the influence of English and German on the Russian language during the English learning based on lexical borrowings in the field of economics. This paper discusses the use and recognition of borrowings from the English and German languages by Russian native speakers. The use of lexical borrowings from English and German by Russian people is examined across age groups, occupations and knowledge of foreign languages. The main goal of this study is to demonstrate that use and recognition of lexical borrowings vary between age groups, occupation and knowledge of foreign languages.Analyzing the results we found out that the age of an individual influences the degree of recognition of borrowings; people connected with the field of economics by their occupation performed more recognized borrowings; the occupation of the participants had more effect than knowledge of goal languages. Fully assimilated words were difficult to identify. The results may benefit English teachers in terms of emphasizing the signs of distinguishing borrowings to English learners, and contributing to using native words.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".