Cognitive, linguistic and print-related predictors of preschool children’s word spelling and name writing
Bibliographic record
Abstract
Preschool children begin to represent spoken language in print long before receiving formal instruction in spelling and writing. The current study sought to identify the component skills that contribute to preschool children’s ability to begin to spell words and write their name. Ninety-five preschool children (mean age = 57 months) completed a battery of cognitive, linguistic, as well as print-related measures, including spelling/writing tasks (i.e. letters, words and name). All writing samples were scored using scoring matrices and inter-rater reliability was 90% and above. Hierarchical linear regression was conducted for word spelling, indicating that after controlling for age and IQ, the model of best fit included expressive vocabulary, working memory, blending, letter naming and letter writing ability. Logistic regression was conducted for name writing, indicating that the model that included age, expressive vocabulary, letter naming and letter writing identified preschool children who wrote their name conventionally and those who could not. Letter writing explained unique variance in both word spelling and name writing, and phonological awareness explained unique variance in word spelling only. These findings suggest that different processes underlie word spelling and name writing, supporting the consideration of a dual-route model of children’s early spelling and writing ability.
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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 | 0.007 |
| 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 | 0.000 |
| 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".