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Record W2808825784 · doi:10.1075/bpa.7.04koh

How do phonological awareness, morphological awareness, and vocabulary knowledge relate to word reading within and between English and Chinese?

2018· book-chapter· en· W2808825784 on OpenAlexaff
Poh Wee Koh, Xi Chen, Alexandra Gottardo

Bibliographic record

VenueBilingual processing and acquisition · 2018
Typebook-chapter
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsWilfrid Laurier UniversityInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsPhonological awarenessLinguisticsVocabularyReading (process)PsychologyWord (group theory)Computer scienceNatural language processingPhilosophy

Abstract

fetched live from OpenAlex

Abstract We discuss the cross-language relationships of phonological awareness, morphological awareness, and vocabulary in the context of English and Chinese and also how these three constructs are related to word reading within and between the two languages. We focused on a series of studies that have examined Chinese and English monolinguals, as well as Chinese-English bilinguals. Research supports the contributions of phonological awareness and morphological awareness to reading in English and Chinese, as well as across the two languages. Findings pertaining to vocabulary, however, have been mixed. The review of research here suggests the need to further investigate the inter-relations among subcomponents of phonological awareness and morphological awareness as well as how different aspects of vocabulary knowledge relate to word reading.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
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 head0.030
GPT teacher head0.315
Teacher spread0.286 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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