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Record W2126344617 · doi:10.3109/02699206.2014.982768

The acquisition of allophones among bilingual Spanish–English and French–English 3-year-old children

2014· article· en· W2126344617 on OpenAlexaff
Andrea A. N. MacLeod, Leah Fabiano‐Smith

Bibliographic record

VenueClinical Linguistics & Phonetics · 2014
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversité de Montréal
FundersNational Institute on Minority Health and Health DisparitiesTemple UniversityNational Institutes of HealthUniversidad Autónoma de Querétaro
KeywordsPsychologyContext (archaeology)Neuroscience of multilingualismLinguisticsAge of AcquisitionContrast (vision)Computer scienceArtificial intelligenceGeographyCognition

Abstract

fetched live from OpenAlex

Children are exposed to highly variable input from multiple sources within their speech community. This study examines the acquisition of allophones in Spanish and French by monolingual and bilingual children. We hypothesised that two factors would influence allophone acquisition: (1) the amount of exposure to phonological input, and (2) the degree of variability of the allophonic pattern. Thirty-four typically developing 3-year-old participated in the study. The analyses revealed that regardless of the language, the monolingual children produced similar error rates in the production of the target allophones. In contrast, the bilingual children produced different patterns of acquisition of the allophones: the Spanish-English bilinguals produced higher error rates than the monolinguals, whereas the French-English bilinguals produced lower error rates than the monolinguals. Possibilities for these differences are discussed within the context of structural complexity as well as in light of the effects of between-language interaction on bilingual phonological development.

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)0.001
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.298
Teacher spread0.287 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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