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Record W2153222145 · doi:10.1080/01443410500341098

Relationships Between First and Second Language Phonological Processing Skills and Reading in Chinese‐English Speakers living in English‐Speaking Contexts

2005· article· en· W2153222145 on OpenAlexaff
Alexandra Gottardo, Penny Chiappe, Bernice Yan, Linda S. Siegel, Yan Gu

Bibliographic record

VenueEducational Psychology · 2005
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British ColumbiaWilfrid Laurier University
Fundersnot available
KeywordsOrthographyReading (process)PsychologyLinguisticsPhonologyPhonological awarenessLearning to readPhonetics

Abstract

fetched live from OpenAlex

The relationships between phoneme categorisation, phonological processing, and reading performance were examined in Chinese‐English speaking children in an English‐speaking environment. Second language (L2, i.e., English) phonological processing but not phoneme categorisation was related to L2 reading. First language (L1) oral language skills were related to Chinese reading with L1 phonological processing being related to the Chinese reading task with a strong phonological component (pseudocharacter reading). L1 phoneme categorisation skill was not strongly related to L1 reading. These findings suggest that phonological processing is related to reading tasks with heavy phonological demands, such as reading in an alphabetic orthography or pseudocharacter reading in a nonalphabetic orthography. Exposure to L1 reading might influence processes used by Chinese‐speaking children in an English‐speaking environment.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.343
Teacher spread0.324 · 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

Citations103
Published2005
Admission routes1
Has abstractyes

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