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Cross‐language transfer of morphological awareness in Chinese–English bilinguals

2011· article· en· W1924072852 on OpenAlexaff
Adrian Pasquarella, Xi Chen, Katie Lam, Yang Luo, Gloria Ramírez

Bibliographic record

VenueJournal of Research in Reading · 2011
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsThompson Rivers UniversityUniversity of Toronto
Fundersnot available
KeywordsPsychologyVocabularyPhonological awarenessReading comprehensionComprehensionReading (process)LinguisticsStructural equation modelingTransfer of trainingLiteracyLanguage transferVocabulary developmentPositive transferNonverbal communicationComprehension approachCognitive psychologyLanguage educationComputer scienceMathematics educationDevelopmental psychologyPedagogy

Abstract

fetched live from OpenAlex

This study examined cross‐language transfer of morphological awareness in Chinese–English bilingual children. One hundred and thirty‐seven first to fourth graders participated in the study. The children were tested on parallel measures of compound awareness, vocabulary, word reading and reading comprehension in Chinese and English. They also received measures of English derivational awareness, English phonological awareness and nonverbal reasoning. Structural equation modelling was used to compare a baseline model with only within‐language paths to a model with cross‐language paths. The cross‐language model fit significantly better than the within‐language model, suggesting transfer of morphological awareness between English and Chinese. In particular, we observed a bidirectional relationship between English compound awareness and Chinese vocabulary. Furthermore, English compound awareness was a significant predictor of Chinese reading comprehension. The conditions that support transfer of morphological awareness and the impact of transfer on literacy development in Chinese and English are discussed.

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 score0.019
Threshold uncertainty score0.037

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 studies0.0010.001
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 head0.140
GPT teacher head0.480
Teacher spread0.340 · 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

Citations264
Published2011
Admission routes1
Has abstractyes

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