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Record W3206098931 · doi:10.1177/25138502211020131

Exploring the development of early reading literacy and story narrative among young children

2021· article· en· W3206098931 on OpenAlexaboutno aff
Chuanjiang Li, Zhaojing Ma, Xinmei Xi

Bibliographic record

VenueJournal of Chinese Writing Systems · 2021
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
FundersChina Postdoctoral Science Foundation
KeywordsReading (process)NarrativeLiteracyStorytellingPsychologyReading comprehensionDevelopmental psychologyComprehensionLanguage developmentPedagogyLinguisticsLiteratureArt

Abstract

fetched live from OpenAlex

While numerous studies have examined the development of reading literacy and language skills of older primary and middle school-aged children, comparably little research focuses on those of younger children. The present study investigates the links between early reading literacy – including reading behavior, comprehension, and engagement–and narrative skills among children aged from 3 to 6. Eighty-five children participated in a picture book reading activity and a storytelling task. Their early reading literacy was rated during a child-led picture book reading, and their narrative skills were scored using the Edmonton Narrative Norms Instrument. Although the children’s development varied greatly in three early reading literacy sub-dimensions, we found a significant developmental tendency and a correlated relationship between early reading literacy and narrative skills among young children. The preschool children’s reading initially focused on pictures and gradually transferred to print as their age increased. Moreover, their reading behavior, comprehension and engagement had a predictive effect on narrative structure and linguistic development. This study suggests that school-based practices for early reading instruction should focus on children’s reading literacy and narrative skills in the future.

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)0.002
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 score0.318
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.301
Teacher spread0.273 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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