Об особенностях идиолексикона носителя городского просторечия
Bibliographic record
Abstract
Vernacular-2, which is a type of urban vernacular, is a widely jargonized variety of oral speech characteristic of unor low-educated citizens. This article presents the results of the research on the vernacular-2 speaker's idio-lexicon. The informant, L.L., is a low-skilled Russian male worker, born in 1959, who has completed 6 years of school. The audio-scripts of the informant's spontaneous oral speech provided material for this study, which was conducted within the framework of linguistic personality research. My study points out the following specifics of the informant's idiolexicon: First, the informant's idiolexicon includes a lot over 350 urban colloquialisms. L.L. extensively uses diminutive forms of personal names, which is also specific of urban vernacular. Obscenisms account for 86 words and expressions with 10% being scatologisms and the rest mat (Russian profane language). The informant typically uses tabooed lexicon as parasite words or to convey non-profane meaning. Second, there is a distinct dialectal presence in the urban colloquialisms of L.L.'s idiolexicon: dialectal-colloquial lexemes constitute approximately one third of all urban colloquialisms. However, the percentage of exclusively dialectal words and expressions is insignificant, which is typical of urban vernacular-2. Third, the informant's idiolexicon contains over 200 jargonisms, predominantly of juvenile and jail variety, with 10 expressions being exclusively jail jargonisms. L.L.'s vocabulary also features another, exiguous group of professional jargonisms, such as jargon of drivers, photographers, electricians, plumbers, and security officers, which dealt or were connected to the informant's professional field. Forth, the idiolexicon of the studied linguistic personality is characterized by great expressiveness. L.L. uses all sub-systems of language; his expressive vocabulary contains not only urban colloquialisms, but also literary, jargon, and dialectal words and expressions, predominantly dealing with the semantic field ''human being''. Among the informant's emotive / evaluative words and expressions I identified a large lexicosemantic group of pejoratives describing people, which is typical of urban vernacular and is culturally predetermined. However, the ratio analysis between the usage of pejoratives and melioratives (including diminutives) shows that in L.L's idiolexicon melioratives prevail. Fifth, typical of urban vernacular in general, the informant's speech displays occasional semantically incorrect usage of borrowed and bookish words. These specifics are admittedly typical for Russian urban vernacular speaker's idiolexi-con.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".