Bibliographic record
Abstract
The article is devoted to the actual problem of changing the language policy, which has been actively discussed by linguists in the last quarter of a century in the subjects of the Russian Federation and in the national states of the near abroad. The importance of finding an answer to the problems associated with the changing language situation in the post-Soviet space is due to intergenerational ties, continuity of development and continuity of cultural heritage. The main attention is paid to the analysis and assessment of the formation of national languages in the subjects of the Russian Federation and in the new national states adjacent to the Russian Federation. The study is preceded by a necessary excursion into the history of the issue and an assessment of the state of development of this scientific problem. A critical assessment of the significance of individual fragments of the language policy of national entities both within the Russian Federation and in neighboring states is proposed. The main stages of the struggle for the native language in the emerging national states of the post-Soviet space are analyzed. The results of the study are important for taking adequate measures in the field of language policy and language construction in the Russian Federation, which is the key to preserving national and state unity.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".