The Spelling Skills of French‐Speaking Dyslexic Children
Bibliographic record
Abstract
Learning to spell is very difficult for dyslexic children, a phenomenon explained by a deficit in processing phonological information. However, to spell correctly in an alphabetic language such as French, phonological knowledge is not enough. Indeed, the French written system requires the speller to acquire visuo-orthographical and morphological knowledge as well. To date, the majority of studies aimed at describing dyslexic children's spelling abilities related to English and reading. The general goal of this study is to describe the spelling performance, from an explanatory perspective, of 26 French-Canadian dyslexic children, aged 9 to 12 years. The specific goals are to describe the spelling performances of these pupils in the context of free production and to compare them with the performances of 26 normally achieving children matched on age (CA) and with those of 29 younger normally achieving children matched on reading age (RA). To do so, errors were classified according to phonological, visuo-orthographical and morphological properties of French written words. The results indicate that the dyslexic group scored lower than the CA group but sometimes also lower than the RA group. The results are discussed according to the types of knowledge required to spell correctly in French.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".