Does the Use of Complex Sentences Differentiate Between Bilinguals With and Without DLD? Evidence From Conversation and Narrative Tasks
Bibliographic record
Abstract
Over-identification of language disorder among bilingual children with typical development (TD) is a risk factor in assessment. One strategy for improving assessment accuracy with bilingual children is to determine which linguistic sub-domains differentiate bilingual children with TD from bilingual children with developmental language disorder (DLD). To date, little research on sequential bilinguals with TD and DLD has focussed on complex (multi-clausal) sentences in naturalistic production, even though this is a noted domain of weakness for school-age monolinguals with DLD. Accordingly, we sought to determine if there were differences in the use of complex sentences in conversational and narrative tasks between school-age sequential bilinguals with TD and with DLD at the early stages of L2 acquisition. We administered a conversation and a narrative task to 63 English L2 children with TD and DLD, aged 5–7 years with 2 years of exposure to the L2. Children had diverse first language backgrounds. The L2-TD and L2-DLD groups were matched for age, length of L2 exposure and general L2 proficiency (receptive vocabulary size). Language samples from both tasks were coded and analyzed for the use of complex versus simple sentences, for the distribution of complex sentence types, for clausal density and mean length of utterance (MLU). Complex sentences included coordinated clauses, sentential complement clauses, adverbial clauses and relative clauses. Using regression modelling and PERMANOVA, we found that the L2-TD group produced more complex sentences than the L2-DLD group, with coordinated clauses, adverbial clauses and relative clauses differing the most between the groups. Furthermore, the two groups differed for mean clausal density, but not for MLU, indicating that clausal density and MLU did not estimate identical morphosyntactic abilities. Individual variation in complex sentence production for L2-TD was predicted by longer L2 exposure and task; by contrast, for L2-DLD, it was predicted by older age. This study indicates that complex sentence production is an area of weakness for bilingual children with DLD, as it is for monolinguals with DLD. The clinical implications of these findings are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".