Orthographic processing and children’s word reading
Bibliographic record
Abstract
ABSTRACT Theories of reading development generally agree that, in addition to phonological decoding, some kind of orthographic processing skill underlies the ability to learn to read words. However, there is a lack of clarity as to which aspect(s) of orthographic processing are key in reading development. We test here whether this is orthographic knowledge and/or orthographic learning. Whereas orthographic knowledge has been argued to reflect a child’s existing store of orthographic representations, orthographic learning is concerned with the ability to form these representations. In a longitudinal study of second- and third-grade students, we evaluate the relations between these two aspects of orthographic processing and word-reading outcomes. The results of our analyses show that variance captured by orthographic knowledge overlaps with that of word reading, to the point that they form a single latent word-reading factor. In contrast, orthographic learning is distinctive from this factor. Further, structural equation modeling demonstrates that early orthographic learning was related to gains in word reading skills. We discuss the implications of these findings for 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 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".