Predicting the early growth of word and nonword reading fluency in a consistent syllabic orthography
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".