French-English Bilingual Children With SLI
Bibliographic record
Abstract
The goal of this study was to determine whether bilingual children with specific language impairment (SLI) are similar to monolingual age mates with SLI, in each language. Eight French-English bilingual children with SLI were compared to age-matched monolingual children with SLI, both English and French speaking, with respect to their use of morphosyntax in language production. Specifically, using the extended optional infinitive (EOI) framework, the authors examined the children's use of tense-bearing and non-tense-bearing morphemes in obligatory context in spontaneous speech. Analyses revealed that the patterns predicted by the EOI framework were borne out for both the monolingual and bilingual children with SLI: The bilingual and monolingual children with SLI showed greater accuracy with non-tense than with tense morphemes. Furthermore, the bilingual and monolingual children with SLI had similar mean accuracy scores for tense morphemes, indicating that the bilingual children did not exhibit more profound deficits in the use of these grammatical morphemes than their monolingual peers. In sum, the bilingual children with SLI in this study appeared similar to their monolingual peers for the aspects of grammatical morphology examined in each language. These bilingual-monolingual similarities point to the possibility that SLI may not be an impediment to learning two languages, at least in the domain of grammatical morphology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".