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Record W4402840318 · doi:10.1086/731661

Pitch Patterns in Standard Negation in Alaskan Dene and the Development of Grammatical Tone

2024· article· en· W4402840318 on OpenAlexaff
Olga Lovick, Siri G. Tuttle

Bibliographic record

VenueInternational Journal of American Linguistics · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNegationTone (literature)LinguisticsComputer scienceNatural language processingPhilosophy

Abstract

fetched live from OpenAlex

We describe a prosodic pattern that is part of standard negation in five Alaskan Dene languages spoken along the Tanana River. In all of these varieties, this “negative high” differs from the tonal distinction originating from historical constriction. We argue that it originates as an emphatic form of negative verb suffixation that is still partially productive in Koyukon. In the other Tanana languages, the negative high occupies different places in the grammar. In Upper Tanana it is a floating tone which associates with the negative verb stem. In Lower Tanana it is a high tone associated with the negative suffix. In Tanacross and Middle Tanana, the negative high is best analyzed as an utterance-level intonational pattern. To our knowledge, this type of intonational source for tone has not been described for other language families. Our study also has implications for typological studies of standard negation and for language teaching.

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)0.001
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.293
Teacher spread0.276 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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