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Record W4391959809 · doi:10.1080/17549507.2023.2287426

Phonemic and pitch variability in bilingual preschoolers: A comparison of Jamaican Creole and English

2024· article· en· W4391959809 on OpenAlexaff
Amna S. Mira, Mary E Wilkens, Karla N. Washington, Victoria S. McKenna

Bibliographic record

VenueInternational Journal of Speech-Language Pathology · 2024
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
FundersNational Institute on Deafness and Other Communication DisordersSaudi Arabian Cultural MissionAmerican Speech-Language-Hearing Association
KeywordsCreole languageLinguisticsPsychologyPhonological developmentPhonologyPhilosophy

Abstract

fetched live from OpenAlex

Purpose The purpose of this study was to investigate the cross-linguistic influences of Jamaican Creole (JC) and English on phonemic and vocal development in bilingual JC-English-speaking preschoolers.Method Sixteen typically developing children (12 females, M = 4 years; 4 months) completed the Diagnostic Evaluation of Articulation in Phonology Word Inconsistency Assessment subtest in JC and in English. Acoustic measures of voice onset time (VOT), VOT variability (VOT SD), mean fundamental frequency (fo), and fo variability (fo SD) were extracted from each target word. Prevoicing was noted. Mixed models and regression models were analysed to understand the patterns of acoustic measures in each language, and the relationship between phonemic and vocal variability, respectively.Result Analyses showed a significant effect of language on fo SD, wherein SD was greater in English than JC. JC spoken (percentage) was a significant positive predictor of VOT SD for voiced (short lag) productions. There was no relationship between phonemic and vocal variability measures.Conclusion Greater fo SD in English may be due to linguistic fo differences and speaking environment. Variability for voiced VOT is likely due to the continued maturation of vocal and articulatory control when children are developing adult-like productions, though longitudinal studies are needed.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.530
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.395
Teacher spread0.378 · 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 teacher head, 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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