Discriminating Children With Language Impairment Among English-Language Learners From Diverse First-Language Backgrounds
Bibliographic record
Abstract
PURPOSE: In this study, the authors sought to determine whether a combination of English-language measures and a parent questionnaire on first-language development could adequately discriminate between English-language learners (ELLs) with and without language impairment (LI) when children had diverse first-language backgrounds. METHOD: Participants were 152 typically developing (TD) children and 26 children with LI; groups were matched for age (M = 5;10 [years;months]) and exposure to English (M = 21 months). Children were given English standardized tests of nonword repetition, tense morphology, narrative story grammar, and receptive vocabulary. Parents were given a questionnaire on children's first-language development. RESULTS: ELLs with LI had significantly lower scores than the TD ELLs on the first-language questionnaire and all the English-language measures except for vocabulary. Linear discriminant function analyses showed that good discrimination between the TD and LI groups could be achieved with all measures, except vocabulary, combined. The strongest discriminator was the questionnaire, followed by nonword repetition and tense morphology. CONCLUSION: Discrimination of children with LI among a diverse group of ELLs might be possible when using a combination of measures. Children with LI exhibit deficits in similar linguistic/cognitive domains regardless of whether English is their first or second language.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".