Yup’ik Loanword Etymologies for the Yukaghir Languages and Dialects
Bibliographic record
Abstract
In this paper, up to twenty-eight new Yukaghir etymologies are described as Eskimo borrowings into the Yukaghir languages and dialects of far northeastern Siberia, with phonological and semantic considerations for each suggestion. These findings provide new insights into the historical phonology of these ancient borrowings as well as fairly clear etymologies for a number of isolated Yukaghir words. The chronology of the borrowings is also considered, and various factors reveal two different competing hypotheses: the Yukaghir correspondences have either resulted from chronologically different borrowing layers through the ages, or the correspondences actually represent the remnants of an ancient genetic language affiliation between the two, a hypothesis supported by the very divergent phonological shapes and semantics of the correspondences. It is argued that the Eskimo correspondences are invariably of the Yup’ik variety (instead of the Inuit variety), and that Yup’ik language(s) were spoken in much earlier times around the Kolyma River, where Yukaghir is still spoken, and in particular close to the Tundra Yukaghirs. The semantic categorization of the borrowings places most of them as elementary phenomena, actions, and perceptions, and if not actually describing an actual genetic language relationship, this at least suggests very intense linguistic contacts between Yup’ik and Yukaghir under bi- or multi-lingual conditions, such as through tribal marriages and where code-switching was the norm for generations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".