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Record W4323029076 · doi:10.7202/1096507ar

Yup’ik Loanword Etymologies for the Yukaghir Languages and Dialects

2023· article· en· W4323029076 on OpenAlexvenueaboutno aff
Peter Sauli Piispanen

Bibliographic record

VenueÉtudes/Inuit/Studies · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and Cultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsVariety (cybernetics)CategorizationHistoryLoanwordPhonologyComputer sciencePhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.147
GPT teacher head0.354
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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