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Early English Immersion and Literacy in Xi'an, China

2007· article· en· W2000750799 on OpenAlexaff
Ellen Knell, Haiyan Qiang, Miao Pei, Yanping Chi, Linda S. Siegel, Lin Zhao, Wei Zhao

Bibliographic record

VenueModern Language Journal · 2007
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVocabularyPhonological awarenessPsychologyLiteracyChinaLinguisticsFirst languageForeign languageFrench immersionNeuroscience of multilingualismMathematics educationPedagogyHistory

Abstract

fetched live from OpenAlex

Instruction in English as a foreign language at an early age is becoming more common worldwide even though the effects of this early instruction are not yet known. This study investigated the English and Chinese language performance of students enrolled in early English immersion in a Chinese primary school. In addition, factors that could predict successful English word recognition were investigated. There were 183 participants who were tested in both Chinese and English word identification, phonological awareness, and vocabulary, as well as English oral proficiency and letter name knowledge. The immersion students performed significantly better than the non‐immersion group on measures of English vocabulary, word identification, and oral proficiency, without any detrimental effects on their Chinese character reading, which made the program, in effect, an additive bilingual system. In addition, phonological awareness and letter name knowledge proved to be strong predictors of English word identification for the immersion students, a finding that was similar to results obtained in studies of native English‐speaking children. The findings have potentially useful pedagogical applications.

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)0.001
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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.305
Teacher spread0.297 · 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

Citations71
Published2007
Admission routes1
Has abstractyes

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