<i>vgck</i> versus <i>vack</i>: The contributions of children's early sub‐lexical orthographic knowledge to gains in word reading
Bibliographic record
Abstract
Background Young children clearly know quite a bit about the conventions of written language; for instance, 5‐year‐old children are sensitive to the fact that words tend to include both consonants and vowels, rather than just one or the other. The core theoretical debate lies in whether this understanding of sub‐lexical orthographic regularities predicts children's reading development. To provide empirical data on this question, we examined whether individual differences in sub‐lexical orthographic knowledge were related to gains in word reading over a year. Methods We measured sub‐lexical orthographic knowledge in Grade 1 by asking children to choose which of two letter‐strings looked most word‐like: one containing vowels and consonants and one containing all consonants or all vowels (e.g., vack vs vgck or uaie, respectively). Children completed control measures of phonological awareness, vocabulary and nonverbal ability in Grade 1. Word reading was measured in both Grades 1 and 2. Results Linear regression analyses identified a small but significant and unique contribution of sub‐lexical orthographic knowledge in Grade 1 to word reading in Grade 2, after controls for the above measures as well as age, parental education and the auto‐regressor of Grade 1 word reading. Conclusions This finding suggests a role for knowledge of sub‐lexical orthographic regularities in children's gains in word reading.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".