Phonological Awareness and Rapid Automatized Naming as Longitudinal Predictors of Reading in Five Alphabetic Orthographies with Varying Degrees of Consistency
Bibliographic record
Abstract
Although phonological awareness (PA) and rapid automatized naming (RAN) are confirmed as early predictors of reading in a large number of orthographies, it is as yet unclear whether the predictive patterns are universal or language specific. This was examined in a longitudinal study across Grades 1 and 2 with 1,120 children acquiring one of five alphabetic orthographies with different degrees of orthographic complexity (English, French, German, Dutch, and Greek). Path analyses revealed that a universal model could not be confirmed. When we specified the best-fitting model separately for each language, RAN was a consistent predictor of reading fluency in all orthographies, whereas the association between PA and reading was complex and mostly interactive. We conclude that RAN taps into a language-universal cognitive mechanism that is involved in reading alphabetic orthographies (independent of complexity), whereas the PA–reading relationship depends on many factors like task characteristics, developmental status, and orthographic complexity.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".