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Record W1588512110 · doi:10.7202/013946ar

Contact-induced lexical development in Yupik and Inuit languages

2006· article· en· W1588512110 on OpenAlexvenueaboutno aff
Anna Berge, Lawrence D. Kaplan

Bibliographic record

VenueÉtudes/Inuit/Studies · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicLexicography and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCoining (mint)LinguisticsPeriod (music)Language contactHistoryDanishLexical itemColonialismArtArchaeology

Abstract

fetched live from OpenAlex

Lexical change in Yupik and Inuit languages was relatively slow until the period of widespread cultural change brought about by contact with Europeans over the past few centuries, probably because there had been little earlier contact with other language families. The colonial period brought various groups to the Arctic and different waves of language contact, primarily with Danish, French, English, and Russian. Lexical borrowing has been significant, and old borrowings, often the result of early trade, can be distinguished from later ones and often pertain to food, tobacco, tools, fabric and other areas where new goods were introduced. Later borrowings came about largely when European political structures were set up and may be less thoroughly integrated phonologically than older borrowings. Numbers of borrowings can be taken to reflect the extent of the foreign contact, as is clearly the case with Russian words in Alaskan languages, most numerous in Aleut, which had the most sustained Russian presence. New religious terms to describe Christianity came into the languages during the colonial period, sometimes as borrowings, but also as relexicalizations of old words pertaining to shamanism. A third means of lexical expansion is coinage, where new terms are invented based on native roots and suffixes. The languages and dialects may thus develop words for the same object or concept by borrowing from different European languages, by relexicalizing an old word, or by coining a new one, with a different result in each case. Different sources for new lexical items have resulted in an important level of differentiation among the languages, and this differentiation needs to be recognized.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.049
GPT teacher head0.290
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2006
Admission routes2
Has abstractyes

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