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Record W3126904475 · doi:10.1017/s0305000920000768

Phonetic discrimination, phonological awareness, and pre-literacy skills in Spanish–English dual language preschoolers

2021· article· en· W3126904475 on OpenAlexaff
Sara A. Smith, Sibylla Leon Guerrero, Sarah Surrain, Gigi Luk

Bibliographic record

VenueJournal of Child Language · 2021
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyLinguisticsNeuroscience of multilingualismDual languageVariation (astronomy)Phonological awarenessPhonemic awarenessLiteracyMathematics educationPedagogy

Abstract

fetched live from OpenAlex

The current study explores variation in phonemic representation among Spanish-English dual language learners (DLLs, n = 60) who were dominant in English or in Spanish. Children were given a phonetic discrimination task with speech sounds that: 1) occur in English and Spanish, 2) are exclusive to English, and 3) are exclusive to Russian, during Fall (age m = 57 months) and Spring (age m = 62 months, n = 42). In Fall, English-dominant DLLs discriminated more accurately than Spanish-dominant DLLs between English-Spanish phones and English-exclusive phones. Both groups discriminated Russian phones at or close to chance. In Spring, however, groups no longer differed in discriminating English-exclusive phones and both groups discriminated Russian phones above chance. Additionally, joint English-Spanish and English-exclusive phonetic discrimination predicted children's phonological awareness in both groups. Results demonstrate plasticity in early childhood through diverse language exposure and suggest that phonemic representation begins to emerge driven by lexical restructuring.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.283
Teacher spread0.278 · 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.

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

Citations8
Published2021
Admission routes1
Has abstractyes

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