Антропонимическая система томских татар XVIII века
Bibliographic record
Abstract
The article is devoted to studying of personal names of Tomsk Tatars of the first quarter of the XVIII century. The research objective is a complex description of functioning of the anthroponimics of Tomsk Tatars of the XVIII centuries in historical and linguistic aspect. In this research the general methods of linguistic research are used: descriptive, contrastivecomparative, structural, techniques of the system and functional analysis. On the basis of these archival materials the features of antroponimic system of Tomsk Tatars are considered, comparison with the systems of other people that allow to allocate their ethnocultural ties during the studied period is carried out. It is revealed that in the names of Tomsk Tatars are widely used the names charms formed from verbal components. On the materials the close interrelation of personal names of Tomsk Tatars with Turkic appellative lexicon is revealed. Many names of Tomsk Tatars became a genetic basis of many Tatar and Russian surnames which remained up to now.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.033 |
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; both teacher heads agree on what is shown here.
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".