The “Sound of Silence”: Sensitivity to Silent Letters in Children With and Without Developmental Language Disorder
Bibliographic record
Abstract
Purpose Children with developmental language disorder (DLD) demonstrate general spelling difficulties. This study investigated accuracy on and sensitivity to silent letters in spelling in children with and without DLD. Investigating silent-letter production provides a window into orthographic and morphological knowledge and enhances understanding of children's spelling skills. Method A group of children with DLD ( M age = 9;11 [years;months]) and two control groups of typically developing children ( n = 30 in each group) were given a dictated spelling task of 44 words that each contained a derivational or a nonderivational silent letter. We coded the silent letter in each word and counted 1 point for each correctly spelled letter in order to examine accuracy on silent letters. Two error patterns were distinguished to analyze sensitivity to silent letters: silent-letter substitutions and silent-letter omissions. Results Repeated-measures ANOVA showed that children with DLD produced significantly more errors on silent letters than did both control groups. Both control groups showed a greater sensitivity to silent-letter endings, as they tended to substitute incorrect silent letters where they made errors. In contrast, children with DLD tended to omit silent letters in their spelling attempts. Conclusions Our results suggest that silent-letter production is a major source of difficulty for spellers, especially for those with DLD, who appear to lack sensitivity to silent letters. These results highlight the importance of promoting spelling instruction to enhance orthographic knowledge in children with DLD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".