Supra-Word Units with a Turkic Component
Bibliographic record
Abstract
The article presents an analysis of Turkic borrowings in the semantic aspect, particularly features of their functioning within set expressions. The materials for the study are academic dictionaries of the Russian literary language published from the 18th to the 20th centuries. These sources were chosen because the lexical variety of the language becomes, at a particular stage of its development, reflected in lexicographic sources, and most comprehensively – in explanatory dictionaries. This type of reference books is aimed at most fully comprise the everyday vocabulary of a literary language. Comparing the lexicographic sources of various periods, as well as a comparative analysis of the Turkic units contained in them, allows tracing the life of a borrowed word in a language and the stages of its assimilation in the receiving language, and identifying the features of functioning of the Turkic layer in the Russian vocabulary. Supra-word units with a Turkic component are also analyzed from the viewpoint of their lexicographic registration, i.e., in compliance with the parameter indicated in explanatory dictionaries. It was found that during their functioning in the Russian language, Turkisms broaden their sphere of usage and occur within phraseologisms, some of them being registered as early as in the 18th – 19th centuries.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".