Interactions between bilingual effects and language impairment: Exploring grammatical markers in Spanish-speaking bilingual children
Bibliographic record
Abstract
This study examines the interaction between language impairment and different levels of bilingual proficiency. Specifically, we explore the potential of articles and direct object pronouns as clinical markers of primary language impairment (PLI) in bilingual Spanish-speaking children. The study compared children with PLI and typically developing children (TD) matched on age, English language proficiency, and mother's education level. Two types of bilinguals were targeted: Spanish-dominant children with intermediate English proficiency (asymmetrical bilinguals, AsyB), and near-balanced bilinguals (BIL). We measured children's accuracy in the use of direct object pronouns and articles with an elicited language task. Results from this preliminary study suggest language proficiency affects the patterns of use of direct object pronouns and articles. Across language proficiency groups, we find marked differences between TD and PLI, in the use of both direct object pronouns and articles. However, the magnitude of the difference diminishes in balanced bilinguals. Articles appear more stable in these bilinguals and therefore, seem to have a greater potential to discriminate between TD bilinguals from those with PLI. Future studies using discriminant analyses are needed to assess the clinical impact of these findings.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".