The <i>magic</i> in <i>magician</i>: Contributions of phonological dimensions of morphological awareness to children's reading development
Bibliographic record
Abstract
Background Oral language has long been acknowledged as a prominent influence on children's reading development. Here, we examine the intersecting contribution of two prominent aspects of oral language – phonology and morphology. We explore this interface by examining contributions from the two dimensions of phonology – phonemic and prosodic – of morphological awareness on children's reading development. Methods In a longitudinal study, we track the word reading and reading comprehension development of 175 children in Grades 3 and 4 (Time 1) over the course of 11 months into Grades 4 and 5 (Time 2), respectively. At Time 1, children also completed a measure of morphological awareness with items varying across the two intersecting phonological dimensions: phonemic and prosodic changes. Results We found two unique effects accounting for gains in reading skill over 1 year after controlling for vocabulary, phonological awareness and nonverbal ability, and the appropriate auto‐regressor. Gains in word reading skill were predicted by performance on morphological awareness items with phonemic changes. Gains in reading comprehension skill were predicted by performance on morphological awareness items with both phonemic and prosodic changes. Conclusions These findings point to key differences in the oral language skills that drive the development of word reading versus reading comprehension and encourage us to consider the rich intersection between features of oral language in understanding children's reading development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".