French argotology in Russia in the first quarter of the 21st century
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the anniversary year of Eda Moiseevna Beregovskaya, it is important to note the continued interest in the study of both the features of social variability of the national language and the expressive capabilities of elements of a specific lexical continuum in literary and journalistic texts. The method of systematic description of French argotisms, which has no analogues in the world practice, proposed by the founder of the national school of argotology, has been repeatedly applied both to the characteristics of a certain sublanguage and to the comparative analysis of several social variants of the national language. Moreover, the scientific heritage of E.M. Beregovskaya contains an array of information on the development and testing of an algorithm for analyzing argotic units in the fabric of a literary text. The analysis allows the addressee to understand words of connivance in context and adequately assess the stylistic effect arising from their presence. The article also describes a convergent approach to the selection of areas of research (lexicological, linguostylistic, field research), which contributes to the inventory of linguistic facts and the identification of universal and specific features of the functioning of elements of argotic vocabulary. Special attention is paid to the followers of E.M. Beregovskaya, who implement individual projects for the study of substandard lexemes as part of the work of the International Research Laboratory of Orel State University named after I.S. Turgenev “Problems of Social and Territorial Heteroglossia”.
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.
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 it