Morphological Errors in Monolingual Spanish-Speaking Children With and Without Developmental Language Disorders
Bibliographic record
Abstract
Purpose The purpose of this study was to identify which morphological markers have the best diagnostic accuracy to identify developmental language disorders (DLD) in monolingual Spanish-speaking children. Method The participants in this study included 50 Spanish-speaking monolingual children with ( n = 25) and without ( n = 25) DLD. Data collection took place in Mexico. Children were administered a comprehensive elicitation task that set up felicitous contexts to produce morphological structures previously identified as problematic for Spanish-speaking children with DLD: articles, direct object pronouns, adjectives, plurals, verb conjugations, and the subjunctive in Spanish. Results Statistically significant group differences between children with and without DLD were found for all morphological structures examined but plurals. Logistic regression analyses suggested that a model that included clitic and verbs was the best model to uniquely predict group membership. This model showed sensitivity of 96% and specificity of 80%. Conclusion Clitics and verbs should be considered morphological markers of DLD in monolingual Spanish-speaking children.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".