Are Dyslexic Children Sensitive to the Morphological Structure of Words When They Read? The Case of Dyslexic Readers of French
Bibliographic record
Abstract
Typically, research has cited a deficient use of word recognition procedures mainly caused by a phonological deficit as the source of dyslexic students' reading difficulties. However, recent studies have shown that morphological processing also plays an important part in reading. In the present study, sensitivity to the morphological structure of words was assessed with a plausibility judgment task, where participants determined which of two pseudo-words most resembled a real word in French, and with a decomposition task requiring participants to extract the base forms of morphologically complex words. Dyslexic participants (DYS, n = 26) aged 9-12 years were matched to 26 participants of the same chronological age (CA) and 30 younger participants of the same reading age (RA). Overall, the decomposition task was less successful at demonstrating morphological knowledge than the plausibility judgment task. Results indicate that dyslexic participants demonstrated some morphological sensitivity, particularly on the plausibility task, but were outperformed by both control groups on both tasks. Performance on morphological tasks was significantly correlated to reading comprehension scores. More research needs to be carried out to better comprehend the effects of task characteristics on dyslexic participants' success and before claiming a different or deviant developmental path for morphological knowledge.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 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 | 0.000 |
| 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".