Bibliographic record
Abstract
The article is devoted to the history of dialectological studies of the Even (Lamut) language. The first information about the Even language was published at the end of the 17th century in Amsterdam in 1692. In the 18th - 19th centuries, foreign and domestic scientists continued collecting and publishing materials on different dialects and speeches of various local groups of Evens in Siberia, the North and the Far East. Only in the Soviet period of the history of the language in the 20th century was the beginning of systematic scientific research in this area. V. G. Bogoraz, K. A. Novikova, V. D. Lebedev, V. A. Robbek, H. I. Dutkin and others published information on the language of the Evens of Kolyma, Magadan Region, Yakutia. In the first quarter of the 21st century, dialectological works were carried out at a new scientific and methodological level by S. I. Sharina, R. P. Kuzmina. These works cover issues of phonetics, vocabulary, morphology and syntax of the modern Even language. As a rule, monographs of these Even scholars published dictionaries (glossaries) reflecting the features of certain dialects of the language. There is no consensus in the literature on the number of adverbs, dialects and subdialects of the Lamut language. This is due to the varying degrees of study of the problems of dialectology in the diachronic aspect. In the fundamental two-volume work "Comparative Dictionary of the Tungus-Manchu Languages" (ed. by Doctor of Philosophy, Professor V. I. Tsinitsus. L.: Nauka, 1975-1977), researchers distinguish 15 dialects and subdialects within three dialects: Western, Middle, Eastern. A. A. Burykin, S. I. Sharina define more than 20 dialects. The study of dialects of the Even language is carried out by scientists from Yakutsk, St. Petersburg and Novosibirsk.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".