MétaCan
Menu
Back to cohort
Record W2048016174 · doi:10.1075/wll.17.1.06hip

The effects of bilingual education on the English language and literacy outcomes of Chinese-speaking children

2014· article· en· W2048016174 on OpenAlexaffabout
Kathleen Hipfner-Boucher, Katie Lam, Becky Xi Chen

Bibliographic record

VenueWritten Language & Literacy · 2014
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyFrench immersionVocabularyLiteracyPhonological awarenessFluencyNeuroscience of multilingualismLinguisticsMathematics educationPedagogy

Abstract

fetched live from OpenAlex

To evaluate the effects of bilingual education on minority-language children’s English language and literacy outcomes, we compared grade 1 Chinese-speaking Canadian children enrolled in three different instructional programs (French Immersion, Chinese-English Paired Bilingual, English-only). ANCOVA results revealed that the French immersion children outperformed the other two groups on measures of English phonological awareness and word reading and that the bilingual groups were comparable to monolingual English norms on a test of receptive vocabulary. Multiple regression analyses were conducted to examine cross-language transfer of skills. French morphological awareness explained unique variance in English word reading and vocabulary for the French immersion group. For the other two groups, Chinese phonological awareness was significantly related to English word reading. Our results suggest that instruction in French or Chinese does not delay the development of early English language and literacy skills for Chinese-speaking children, as the children may be able to leverage skills from their other language to facilitate their English learning. Keywords: Bilingual education; French immersion; cross-language transfer

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 score0.063
Threshold uncertainty score0.126

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.003
GPT teacher head0.291
Teacher spread0.288 · 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

Citations45
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueWritten Language & LiteracySame topicReading and Literacy DevelopmentFrench-language works237,207