Clarifying links to literacy: How does morphological awareness support children’s word reading development?
Bibliographic record
Abstract
Abstract We know a great deal about children’s first steps into reading. Here, we explore how they become more sophisticated readers, learning to read complex words. Theoretical accounts predict that one key factor is morphological awareness, or awareness of the minimal units of meaning in language. And yet empirical studies have yet to clarify whether morphological awareness has a stronger relation to the development of reading skill for words with multiple morphemes in particular (i.e., morphological decoding) or to the reading of a whole range of words. We examined this question in this study by contrasting the role of morphological awareness in the development of morphological decoding and of broader word reading skill. Participants were 197 English-speaking children who were followed from Grade 3 to 4. We conducted longitudinal analyses that included stringent autoregressive controls to capture the determinants of gains over time, as well as controls for vocabulary and phonological awareness. Structural equation modeling (SEM) path analysis with this set of controls revealed that morphological awareness predicted significant unique gains in morphological decoding from Grade 3 to 4 with no such unique contributions to broader word reading skill. These findings clarify the role of morphological awareness in supporting children in developing the ability to read morphologically complex words, supporting a more targeted role for morphology in theories of word 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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".