What is the best way to characterise the contributions of oral language to reading comprehension: listening comprehension or individual oral language skills?
Bibliographic record
Abstract
Educators and researchers agree that oral language is fundamental to students' reading acquisition. It is not clear how best to conceptualise oral language within models of reading – as one's overall understanding of spoken language, or as individual skills, each with unique contributions to children's reading comprehension. In our longitudinal study of children in Grades 2 and 3, we examined the unique contributions of three oral language skills – vocabulary, syntactic awareness, and morphological awareness – to gains in reading comprehension assessed later that academic year ( N = 116) and in the spring of the following academic year ( N = 87). In our most conservative analyses, we controlled for children's listening comprehension in addition to prior reading achievement. Each language skill predicted variance in later reading comprehension beyond that accounted for by initial word reading and reading comprehension. In analyses with listening comprehension also controlled, each of syntactic awareness and morphological awareness retained their predictive power. Morphological awareness emerged as the most robust predictor and was associated with greater increases in reading comprehension for students in third versus second grade. Results support theoretical models that identify and differentiate contributions from individual oral language skills to reading comprehension. Our findings suggest that increasing each of these oral language skills within the elementary classroom may lead to advances in children's reading comprehension.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".