Morphological Errors in Spanish-Speaking Bilingual Children With and Without Developmental Language Disorders
Bibliographic record
Abstract
Purpose The purpose of this study was to find morphological markers with good diagnostic accuracy to identify developmental language disorders (DLD) in Spanish–English bilingual children. Method The participants in this study included 66 Spanish–English bilingual children between the ages of 4;0 and 6;11 (years;months) with ( n = 33) and without DLD ( n = 33). We employed a comprehensive production task in Spanish to elicit morphological structures that have been previously found to be problematic for Spanish-speaking children with DLD. These structures included elements of nominal morphology (articles, direct object pronouns, adjectives, and plurals) and verbal morphology (verbs and the subjunctive mood). Logistic regression was used in this study to find a set of grammatical structures that most accurately predicted group membership. Results Spanish–English bilingual children with and without DLD significantly differed from each other in their accurate production of articles, clitics, adjectives, verbs, and the subjunctive mood. Clitics, verbs, and the subjunctive mood in isolation had adequate diagnostic accuracy. A combination of verb and subjective mood accuracy best predicted group membership in this study (sensitivity of 85% and specificity of 91%). Conclusion In addition to clitics, verbs, and the subjunctive mood, both elements of verbal morphology should be considered grammatical markers of DLD in Spanish–English bilingual children. Supplemental Material https://doi.org/10.23641/asha.13641320
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".