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Record W4210586539 · doi:10.1515/cog-2021-0035

From ‘clubs’ to ‘clocks’: lexical semantic extensions in Dene languages

2022· article· en· W4210586539 on OpenAlexaff
Conor Snoek

Bibliographic record

VenueCognitive Linguistics · 2022
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsMetonymyLinguisticsCognitive linguisticsComputer scienceRoot (linguistics)Cognitive semanticsSemantics (computer science)Lexical semanticsCognitive grammarMeaning (existential)Theoretical linguisticsCognitionLexical itemNatural language processingPsychologyMetaphorPhilosophy

Abstract

fetched live from OpenAlex

Abstract This study examines the semantics of a root form underlying a wide range of Dene lexical expressions. The root evolved from a simple nominal denoting “club” to expressions lexicalizing the movement of stick-like objects and the rotation of helicopter blades. These semantic extensions arise through source-in-target and target-in-source metonymies. Drawing on Cognitive Linguistics, especially the theory of metonymy, offers a method of describing the range of meanings expressed by this root in a concise manner. Focusing on the results of metonymic meaning extensions also opens the way to addressing questions in the history of Dene languages. This study contributes to increasing the typological scope of Cognitive Linguistic approaches and argues for the usefulness of the theory in addressing problems in Dene linguistics.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.011
Scholarly communication0.0040.010
Open science0.0010.004
Research integrity0.0010.003
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 head0.030
GPT teacher head0.344
Teacher spread0.314 · 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 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
Published2022
Admission routes1
Has abstractyes

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