Narrative macrostructure and microstructure profiles of bilingual children with autism spectrum disorder: differentiation from bilingual children with developmental language disorder and typical development
Bibliographic record
Abstract
Abstract Children with autism spectrum disorder (ASD) show heterogeneous language profiles beyond early language delays. Understanding the second language profiles of bilingual children with ASD is important for clinical practice in diverse societies. Accordingly, we examined the narrative abilities of bilinguals with ASD, with developmental language disorder (DLD), and with typical development (TD) to determine which narrative components best differentiate bilinguals with ASD from the other groups. Participants were 29 bilingual children with ASD, DLD, and TD who were matched for age (mean = 6;8), nonverbal intelligence, and receptive vocabulary. Narratives were coded for macrostructure (story grammar (SG) scores, number of individual SG components) and microstructure (syntactic complexity, mean length of utterance, lexical diversity, and story length). The TD group had superior SG scores, included more SG components, and used longer utterances and more complex syntax than the ASD group, whereas no differences were found between the clinical groups. For SG components requiring perspective-taking abilities, the ASD group had worse performance than the TD and DLD groups. Our results suggest that bilingual children with ASD show weaknesses in both macrostructure and microstructure, which can overlap with children with DLD. The linguistic profiles of bilingual children with ASD and DLD are thus both overlapping and distinct.
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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 | 0.000 | 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".