History of the Naukan Yupik Eskimo dictionary with implications for a future Siberian Yupik dictionary
Bibliographic record
Abstract
Naukan is a Yupik Eskimo language spoken now by only a few people on the Russian side of the Bering Strait, but with strong Alaskan affinities. Naukan speaker Dobrieva of Lavrentiya, linguist Golovko of St. Petersburg, and linguists Jacobson and Krauss of Fairbanks have compiled a Naukan dictionary in two parallel volumes: Naukan in a latin-letter orthography to English, and Naukan in the modified Cyrillic alphabet used for Chukotkan Eskimo languages to Russian. It was both appropriate and beneficial that this project involved people from Alaska, European Russia, and Chukotka. The dictionary was recently published by the Alaska Native Language Center of the University of Alaska Fairbanks. The Naukan dictionary in two parallel volumes can serve as a model for a new dictionary of (Central) Siberian Yupik, a language spoken, at least ancestrally, by roughly equal numbers on St. Lawrence Island Alaska and in the New Chaplino-Sirenik area of Chukotka, Russia. Such a dictionary could help to reinvigorate that language and allow it better to serve as a bridge between the two halves of a single people and culture divided only in recent decades by a boundary not of their own making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".