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Record W1982847761 · doi:10.1037/0022-0663.97.4.580

Bilingualism, Language Proficiency, and Learning to Read in Two Writing Systems.

2005· article· en· W1982847761 on OpenAlexaff
Ellen Bialystok, Catherine McBride‐Chang, Gigi Luk

Bibliographic record

VenueJournal of Educational Psychology · 2005
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsYork University
Fundersnot available
KeywordsNeuroscience of multilingualismPsychologyLinguisticsLanguage proficiencyLanguage acquisitionCognitive psychologyMathematics education

Abstract

fetched live from OpenAlex

Two hundred and four 5- and 6-year-olds who were monolingual English-, bilingual English–Chinese-, or Chinese-speaking children beginning to learn English (2nd-language learners) were compared on phonological awareness and word decoding tasks in English and Chinese. Phonological awareness developed in response to language exposure and instruction but, once established, transferred across languages for both bilinguals and 2nd-language learners. In contrast, decoding ability developed separately for each language as a function of proficiency and instruction in that language and did not transfer to the other language. Therefore, there was no overall effect of bilingualism on learning to read: Performance depended on the structure of the language, proficiency in that language, and instructional experiences with that writing system. These results point to the importance of evaluating the features of the languages and instructional context in which children become biliterate.

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)0.004
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.025
GPT teacher head0.445
Teacher spread0.420 · 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

Citations292
Published2005
Admission routes1
Has abstractyes

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