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Record W2899900163 · doi:10.1017/s0142716418000541

Cross-script transfer of word reading fluency in a mixed writing system: Evidence from a longitudinal study in Japanese

2018· article· en· W2899900163 on OpenAlexaff
Tomohiro Inoue, George K. Georgiou, Hirofumi Imanaka, Takako Oshiro, Hiroyuki Kitamura, Hisao Maekawa, Rauno Parrila

Bibliographic record

VenueApplied Psycholinguistics · 2018
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Alberta
FundersJapan Society for the Promotion of Science
KeywordsKanjiFluencyPsychologyReading (process)Verbal fluency testVocabularyCognitive psychologySyllabic verseWriting systemLinguisticsCognitionChinese charactersNeuropsychology

Abstract

fetched live from OpenAlex

ABSTRACT We examined the cross-lagged relations between word reading fluency in the two orthographic systems of Japanese: phonetic (syllabic) Hiragana and morphographic Kanji. One hundred forty-two Japanese-speaking children were assessed on word reading fluency twice in Grade 1 (Times 1 and 2) and twice in Grade 2 (Times 3 and 4). Nonverbal IQ, vocabulary, phonological awareness, morphological awareness, and rapid automatized naming were also assessed in Time 1. Results of path analysis revealed that Time 1 Hiragana fluency predicted Time 2 Kanji fluency after controlling for the cognitive skills. Time 2 Hiragana fluency did not predict Time 3 Kanji fluency or vice versa after the autoregressor was controlled, but Hiragana and Kanji fluency were reciprocally related between Times 3 and 4. These findings provide evidence for a cross-script transfer of word reading fluency across the two contrastive orthographic systems, and the first evidence of fluency in a morphographic script predicting fluency development in a phonetic script within the same language.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.064
GPT teacher head0.377
Teacher spread0.314 · 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.

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

Citations5
Published2018
Admission routes1
Has abstractyes

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