Exploring Predictors of Expressive Grammar Across Different Assessment Tasks in Preschoolers With or Without DLD
Bibliographic record
Abstract
Predictors of expressive grammar were compared in formal and naturalistic assessment tasks for children with typically developing (TD) language and with Developmental Langauge Disorder (DLD). Standardized expressive language assessments were administered to 110 preschoolers. The parents of these children reported whether or not they were concerned about their child’s speech and language development. Stepwise regression analyses revealed receptive language as the only significant predictor of expressive grammar across assessment tasks. For TD preschoolers, receptive vocabulary and grammar accounted for expressive grammar performance in the formal task; however, only receptive grammar accounted for performance in the naturalistic task. For DLD preschoolers, only receptive vocabulary accounted for expressive grammar performance across both tasks. Nonverbal IQ and parent concern did not predict expressive grammar performance in either task. Implications for treatment of preschool DLD using relative strengths in vocabulary are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".