Unraveling the links between rapid automatized naming (RAN), phonological awareness, and reading.
Bibliographic record
Abstract
It is well established that phonological awareness (PA) and rapid automatized naming (RAN) tasks reliably predict children’s developing word reading abilities, across a wide range of languages. However, existing research has not yet demonstrated unequivocally whether RAN and PA are independently and causally linked to reading, nor fully explored the underlying cognitive mechanisms. Most existing research has assessed PA and RAN in children who may already have some reading skill, making direction of influence hard to ascertain. To address this, the current longitudinal research initially assessed RAN and PA in a very young sample of 91 English children (mean age: 3;11; SD = 3.7 months), demonstrated to be non-readers. Children were reassessed on RAN, PA, and word-level reading, 18 months (Time 2) and then a further year later (Time 3). To explore underlying mechanisms, separate measures of reading accuracy and fluency were taken, and reading tasks varied according to the extent to which they required alphabetic decoding and lexical, orthographic knowledge. Path analyses revealed that from Time 1 to Time 2 both RAN and PA predicted word reading, indicating temporal precedence, though there was some degree of reciprocity in these relationships. However, by Time 3, while RAN still predicted accuracy and fluency of reading, PA only predicted reading accuracy. Furthermore, findings suggested that while RAN was robustly related to both alphabetic decoding and lexical, orthographic aspects of reading, PA’s relationship was restricted to alphabetic decoding accuracy. Theoretical and practical implications are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".