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Record W4391734927 · doi:10.1121/10.0024756

Tonguedness in speech: Lateral bias in lingual bracing

2024· article· en· W4391734927 on OpenAlexafffund
Yadong Liu, Jahurul Islam, Kate Radford, Oksana Tkachman, Bryan Gick

Bibliographic record

VenueJASA Express Letters · 2024
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Deafness and Other Communication DisordersNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsTongueSpeech productionPsychologyPreferenceProduction (economics)AsymmetryTip of the tongueCognitive psychologyAudiologyLinguisticsSpeech recognitionComputer scienceMedicinePhilosophyMathematicsPhysics

Abstract

fetched live from OpenAlex

This study examines the lateral biases in tongue movements during speech production. It builds on previous research on asymmetry in various aspects of human biology and behavior, focusing on the tongue's asymmetric behavior during speech. The findings reveal that speakers have a pronounced preference toward one side of the tongue during lateral releases with a majority displaying the left-side bias. This lateral bias in tongue speech movements is referred to as tonguedness. This research contributes to our understanding of the articulatory mechanisms involved in tongue movements and underscores the importance of considering lateral biases in speech production research.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.037
GPT teacher head0.323
Teacher spread0.286 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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