Morphology and Spelling in French: A Comparison of At‐Risk Readers and Typically Developing Children
Bibliographic record
Abstract
We present two studies that examine the role of morphology in French spelling. In Study 1, we examined the concurrent and longitudinal relationships between inflectional awareness and derivational awareness and spelling within a sample of 77 children in a French immersion programme in Canada. Children completed a non-verbal reasoning measure and French measures of phonological awareness, word reading, vocabulary, morphological awareness, and spelling. Results showed that inflectional morphological awareness in Grade 3 was a predictor of spelling in the same grade. Inflectional awareness in Grade 2 predicted Grade 3 spelling, controlling for reading-related skills and spelling at Grade 2. These analyses support the role of inflectional morphological awareness in the development of spelling of children of a range of reading and spelling abilities. In contrast, derivational awareness in Grades 2 and 3 did not predict spelling concurrently in both grades respectively. Study 2 contrasted the morphological errors in the spellings of six children at risk for reading difficulties with those of six chronological age-matched and six reading level-matched children. Analyses showed that at-risk children exhibited more difficulties with spelling roots and suffixes in words as compared with their age-matched peers, although they performed similarly to children matched on reading level. Copyright © 2017 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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".