General Name as Cultural Code of Siberian Tatars
Bibliographic record
Abstract
The article actualizes the problems of studying the pedigree and generic names of the Siberian Tatars in the linguocultural aspect. The source of the research is the Russian-language documents of the regional archive of the XVIII-XIX centuries, which recorded information on the composition of the family of residents of the Otdelno-Babasan volost of the Tobolsk province. Today, the issue of updating and preserving the language and culture of the Siberian Tatars is extremely acute, since the dialects of the natives of Western Siberia, according to UNESCO, are at the stage of extinction. The work has an integrated approach, therefore the following methods are used: the method of linguoculturological analysis, the method of interviewing informants, the method of language coexistence, the genealogical method, the descriptive-analytical method, the method of comparing definitions, the method of etymological analysis, the method of lexicographic description, the quantitative-statistical method The classification of generic names by etymology and structure is made, the models of the name of the deceased relative are identified, the traditions of the name of the deceased by the name of the deceased relative in Turkic cultures are examined, the mechanisms of metaphorization of kinship terms are determined. The authors come to the conclusion that the patrimonial names of the Siberian Tatars are a cultural code that stores echoes of the archaic world, namely the cult of their ancestors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".