Spelling Errors in French‐speaking Children with Dyslexia: Phonology May Not Provide the Best Evidence
Bibliographic record
Abstract
For children with dyslexia, learning to write constitutes a great challenge. There has been consensus that the explanation for these learners' delay is related to a phonological deficit. Results from studies designed to describe dyslexic children's spelling errors are not always as clear concerning the role of phonological processes as those found in reading studies. In irregular languages like French, spelling abilities involve other processes than phonological processes. The main goal of this study was to describe the relative contribution of these other processes in dyslexic children's spelling ability. In total, 32 francophone dyslexic children with a mean age of 11.4 years were compared with 24 reading-age matched controls (RA) and 24 chronological-age matched controls (CA). All had to write a text that was analysed at the graphemic level. All errors were classified as either phonological, morphological, visual-orthographic or lexical. Results indicated that dyslexic children's spelling ability lagged behind not only that of the CA group but also of the RA group. Because the majority of errors, in all groups, could not be explained by inefficiency of phonological processing, the importance of visual knowledge/processes will be discussed as a complementary explanation of dyslexic children's delay in writing. Copyright © 2016 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".