ZOONYMIC VOCABULARY OF THE SIBERIAN TATARS IN THE COMPARATIVE-HISTORICAL ASPECT
Bibliographic record
Abstract
The article analyzes the zoonymic vocabulary of the Siberian Tatars of the late 18th - early 19th centuries and its functioning in the modern Tatar literary language and Siberian dialects. The material of the research is the Russian-Tatar Dictionary, collected at the Tobolsk Main Public School by the Tatar language teacher Joseph Giganov (St. Petersburg, 1804), as well as field records made during expedition trips to settlements with a compact residence of Siberian Tatars in 2020. 9 groups of animal names have been identified, using the biological classification of the animal world. The group of names of invertebrates is supplemented by the names of worms (Vermes), crustaceans (Crustacea), arachnids (Arachnida), insects (Insecta), and vertebrates - the names of fish (Pisces), amphibians (Amphibia), reptiles (Reptilia), birds (Aves) and mammals (Mammalia). In addition, synonymy and variability within the lexical-thematic group are considered from the standpoint of the modern division into literary and dialectal units. The authors come to the conclusion that when compiling synonymous series, the main principles were usuality and bookishness.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".