Phonographic spelling errors in developmental language disorder: Insights from executive functions
Bibliographic record
Abstract
The present study examined the executive functions and spelling performance of children with developmental language disorder (DLD) over a school year. Through a fine-grained spelling error analysis, we investigated whether the measured executive functions would distinguish children's spelling profiles. The study comprised three groups: the DLD-S group (aged 7-9 years), including children with DLD matched on the total number of spelling errors produced on a dictation task with a typically developing group (n = 8); the DLD-AM group (aged 7-9 years), including children with DLD matched on chronological age and phonological awareness skills with the DLD-S group (n = 8); and the typically developing group (n = 16; aged 7-8 years). The results indicated that both DLD groups tended to produce more phonographic errors (i.e., spelling errors that change the phonology of the word) even if the DLD-S group produced a similar number of errors in comparison with the typically developing group. In particular, the DLD-AM group made more phoneme omissions than the other groups. The DLD-AM group also had the smallest spelling improvement over the school year and the weakest updating ability. In contrast, all groups had similar inhibition and cognitive flexibility abilities. This may indicate that some children with DLD present limitations in updating, which may lead to a slower spelling acquisition and a greater number of phonographic errors. Language impairments affect and delay spelling acquisition, and the presence of an updating deficit may exacerbate this delay.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".