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Record W2945121514 · doi:10.1044/2019_jslhr-l-17-0323

From Cantonese Lexical Tone Awareness to Second Language English Vocabulary: Cross-Language Mediation by Segmental Phonological Awareness

2019· article· en· W2945121514 on OpenAlexaff
William Choi, Shelley Xiuli Tong, S. Hélène Deacon

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2019
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsDalhousie University
Fundersnot available
KeywordsVocabularyLinguisticsTone (literature)Phonological awarenessPsychologyStress (linguistics)MediationEnglish vocabularyLiteracy

Abstract

fetched live from OpenAlex

Purpose Cantonese lexical tone awareness is closely associated with 1st language Cantonese vocabulary knowledge, but its role in 2nd language English vocabulary knowledge was unclear. We addressed this issue by investigating whether and, if so, how Cantonese lexical tone awareness contributes to English expressive vocabulary knowledge in Hong Kong Cantonese-English bilingual children. Method A sample of 112 Hong Kong Cantonese-English bilingual 2nd graders were tested on Cantonese lexical tone awareness, English lexical stress sensitivity, Cantonese- English segmental phonological awareness, and both Cantonese and English expressive vocabulary knowledge. Results Structural equation modeling analyses revealed that Cantonese lexical tone awareness contributed indirectly to English expressive vocabulary knowledge through English lexical stress sensitivity and Cantonese-English segmental phonological awareness. Conclusion These results demonstrate the role of Cantonese lexical tone awareness in Cantonese-English bilingual children's English vocabulary knowledge. This also underscores the importance of 1st language suprasegmental phonological awareness in 2nd language expressive vocabulary knowledge.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.035
GPT teacher head0.420
Teacher spread0.385 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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