Comparing Etymological Characteristics of Plant Naming in Yakut, Even, and Evenk Languages
Bibliographic record
Abstract
This article considers issues of language contacts between Yakut, Even, and Evenk people through an analysis of plant vocabulary. We define etymological characteristics of fixed lexical units that denote plant names and present results of a comparative analysis of plant naming in these three languages, with emphasis on lexical parallels and structural types in designating plant names. To our knowledge, this is the first research to undertake a comparative study of plant naming in the Yakut and Tungusic languages (Even and Evenk) with consideration of the methods of their formation. The study is highly relevant because of the unique contribution of plant-world vocabulary in helping to clarify peculiarities of native speakers’ natural environments. Our results show that, based on lexical units with stable semantic meaning, the Evenk language is in the closest position vis-à-vis Yakut. There are 16 names in Yakut of plants and four of common names of herbs that grow on the territory of the Republic of Sakha (Yakutia) and have borrowed names in Evenk and Even. Twenty-eight names have lexical parallels in Evenk (including variations) and two in Even.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".