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Record W4389767044 · doi:10.1515/9783110712742-039

39 Dene – Athabaskan

2023· book-chapter· en· W4389767044 on OpenAlexaboutno aff
Leslie Saxon

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

The Dene language family includes some 40 distinct languages, and varieties of these languages. The family is large both in the number of languages and in the broadly distributed geographical areas of western North America which sustain Dene peoples. The Dene world takes in Dine and Apache language-speaking communities extending across a wide area including the American Southwest; communities of the Pacific Coast area stretching from present-day NW California to southern Washington state; and communities both west and east of the Rocky Mountains north of the present-day Canada-US border and extending from Cook Inlet at the west as far as Hudson Bay at the east. Because of their size, the Dene territories cover many types of terrain and take in many river systems, and the people have many linguistic and cultural neighbours. This chapter sketches phonological, morphological, syntactic, and semantic properties of Dene languages, chosen with reference to what is happening in Dene language communities driving forward language maintenance, revitalization, and reclamation. The syntactic descriptions are more in depth than other areas of grammar. The goals of this chapter are (1) to provide discussions of linguistic topics potentially useful in revitalization work, (2) to share descriptions and terminology with language learners, instructors, and scholars to support their work, (3) to support access to the range of linguistic resources developed over the past 150-200 years, and (4) to provide some current references.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.928
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2290.013

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 head0.080
GPT teacher head0.219
Teacher spread0.139 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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 routes1
Has abstractyes

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