Sentence repetition performance in bilingual children with SLI compared to age and language-matched peers
Bibliographic record
Abstract
Sentence repetition (SR) tasks are a reliable clinical marker of specific language impairment (SLI). It is unknown to what extent memory and accumulated language knowledge are employed in this task and how their breakdown presents in children with SLI. It is also yet unknown whether bilingual children with SLI present with different performance patterns on these tasks compared to age and language-matched peers in terms of performance on syntactic categories of words, position of words in the phrase, and the quality of errors made (substitution or omission). The present study investigated SR task performance by scoring for success in syntactic word categories, accuracy in the first or second half of the phrase, and substitution errors. Results are reported for three large participant groups: twenty-six bilingual children with SLI (mean age=61 months), fifty-five typically developing age-equivalent children comprising monolinguals (n=18, mean age=59 months) and bilinguals (n=47, mean age=58 months) and forty-one younger typically developing children comprising monolinguals (n=17, mean age=36 months) and bilinguals (n=24, mean age=35 months). Compared with the large TD groups, the children with SLI performed significantly worse than TD groups but did not produce error patterns distinguishing them as having weaknesses in particular syntactic categories. Both the younger bilinguals and monolinguals and the SLI group showed recency effects in significantly higher performance on the second half of the phrases. The SLI group made significantly fewer errors of substitutions, with 98.6% of their errors being omissions. Matched groups showed the SLI group performing more similarly in syntactic category scores to language-matched peers than age-and-exposure matched peers. Typically developing bilinguals matched on language ability performed similarly to each other. The data support a multifaceted view of SR with memory and accumulated language knowledge playing key roles as underlying mechanisms, and may indicate that in the impediment of access to accumulated knowledge, children with SLI rely more heavily on short-term memory processes without understanding and reconstructing the phrases, as evidenced by their lack of substitution errors.
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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 | 0.001 |
| 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 | 0.000 |
| 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".