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Record W2105617967 · doi:10.1017/s0142716411000609

The development of young Chinese children's morphological awareness: The role of semantic relatedness and morpheme type

2011· article· en· W2105617967 on OpenAlexaff
Meiling Hao, Xi Chen, Vedran Dronjic, Hua Shu, Richard C. Anderson

Bibliographic record

VenueApplied Psycholinguistics · 2011
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMorphemePsychologyHomophoneLinguisticsClass (philosophy)Developmental psychologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

ABSTRACT The research reported in this paper investigated the effects of semantic relatedness of words (closely related vs. distantly related) and morpheme type (free morpheme vs. bound morpheme) on young Chinese children's homophone awareness, an aspect of morphological awareness, in two experiments. The first experiment was a cross-sectional study including 39 children in a beginning kindergarten class, 39 children in an intermediate kindergarten class, and 36 children in a senior kindergarten class. The second experiment was a 7-month longitudinal study involving 43 first graders and 50 second graders at the beginning of the study. In both experiments, the children judged whether orally presented words shared the same morpheme or contained homophonous morphemes. The results suggest that homophone awareness emerges in Chinese children in the kindergarten years. Children's morpheme identification is facilitated by the semantic proximity of words that share a morpheme, and awareness of free morphemes is developed before that of bound morphemes. Furthermore, although semantic relatedness is the most prominent factor in kindergarten, its effect varies as a function of morpheme type in the early primary grades. Our research sheds light on the developmental course of morphological awareness and the factors that influence it.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.213
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.291
Teacher spread0.269 · 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 teacher head, 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

Citations28
Published2011
Admission routes1
Has abstractyes

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