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Record W3043211377 · doi:10.1111/1467-9817.12317

Predicting the early growth of word and nonword reading fluency in a consistent syllabic orthography

2020· article· en· W3043211377 on OpenAlexaff
Tomohiro Inoue, George K. Georgiou, Naoko Muroya, Miyuki Hosokawa, Hisao Maekawa, Rauno Parrila

Bibliographic record

VenueJournal of Research in Reading · 2020
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsEducation and Early Childhood DevelopmentUniversity of Alberta
FundersJapan Society for the Promotion of Science
KeywordsPsychologySyllabic verseFluencyOrthographyPhonological awarenessReading (process)Rapid automatized namingVocabularyPhonemic awarenessVerbal fluency testCognitionPhonologyLinguisticsCognitive psychologyNeuropsychology

Abstract

fetched live from OpenAlex

Background The present study aimed to examine the early growth of word and nonword reading fluency and their cognitive predictors in a consistent syllabic orthography (Japanese Hiragana ). Method One hundred sixty‐nine Grade 1 Japanese children ( M age = 80.12 months, SD = 3.62) were followed until the middle of Grade 2 and assessed four times on word and nonword reading fluency in Hiragana . Nonverbal IQ, vocabulary, phonological awareness, rapid automatized naming, phonological memory and morphological awareness were also assessed at the beginning of Grade 1. Results Growth curve analysis showed that growth was faster in word reading than in nonword reading and the lexicality effect increased over time. Rapid automatized naming, phonological memory and morphological awareness were associated with the initial status and rate of growth in word and nonword reading. Furthermore, the initial status and the growth rates were highly correlated between word and nonword reading, even when the effects of the cognitive skills were controlled. Conclusions These findings suggest that, despite the remarkable differences in the growth trajectories of word and nonword reading fluency, they share at least a part of their underlying processes and develop closely in tandem during this period.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.023
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.078
GPT teacher head0.385
Teacher spread0.307 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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