Narrative Skills of Bilingual Children with Autism Spectrum Disorder
Bibliographic record
Abstract
The study investigated how narratives are influenced by both autism spectrum disorder (ASD) and bilingualism. We analyzed the short narratives of school-age Quebec French-speaking children: bilinguals with and without ASD, and monolinguals with and without ASD. Children were given sets of three picture cards depicting a scenario, and were asked to sequence the cards and tell a story. We measured: (1) language production (number of utterances, total number of words), (2) macrostructure (appropriate sequencing of events, number of events mentioned, coherence), (3) microstructure (character introductions, maintenance of referential terms, use of grammatical gender, use of connectives), and (4) evaluative devices (both linguistic and non-linguistic), and mental state terms. With respect to language production, bilinguals produced more utterances than monolinguals, despite having marginally lower receptive vocabulary scores in French. With respect to macrostructure, typically-developing children provided more coherent narratives. No significant differences were found on microstructure or evaluative devices, but evaluative devices were infrequent for all groups. There were no decrements in the narratives of bilingual children relative to monolingual children, both with and without ASD; in fact we found an increased number of utterances in the narratives of bilinguals. The current findings suggest that bilingualism does not negatively affect narrative skills in children with ASD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".