Specific Language Impairment in French-Speaking Children: Beyond Grammatical Morphology
Bibliographic record
Abstract
PURPOSE: Studies on specific language impairment (SLI) in French have identified specific aspects of morphosyntax as particularly vulnerable. However, a cohesive picture of relative strengths and weaknesses characterizing SLI in French has not been established. In light of normative data showing low morphological error rates in the spontaneous language of French-speaking preschoolers, the relative prominence of such errors in SLI in young children was questioned. METHOD: Spontaneous language samples were collected from 12 French-speaking preschool-age children with SLI, as well as 12 children with normal language development matched on age and 12 children with normal language development matched on mean length of utterance. Language samples were analyzed for length of utterance; lexical diversity and composition; diversity of grammatical morphology and morphological errors, including verb finiteness; subject omission; and object clitics. RESULTS: Children with SLI scored lower than age-matched children on all of these measures but similarly to the mean length of utterance-matched controls. Errors in grammatical morphology were very infrequent in all groups, with no significant group differences. CONCLUSION: The results indicate that the spontaneous language of French-speaking children with SLI in the preschool age range is characterized primarily by a generalized language impairment and that morphological deficits do not stand out as an area of particular vulnerability, in contrast with the pattern found in English for this age group.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".