Written and Oral Narratives of Children and Adolescents With Down Syndrome
Bibliographic record
Abstract
PURPOSE: This study describes written and spoken narrative skills of school-age individuals with Down syndrome (DS). METHOD: Twenty-one students with DS (age 6;6 [years;months]-19;10) and 17 reading-matched, typically developing (TD) controls (age 4;9-10;9) were matched using Word Identification subtest raw scores (Woodcock Reading Mastery Tests-Revised; R. W. Woodcock, 1987; age equivalents: 5;0-9;7 for both groups). Matching on reading resulted in significantly higher mental ages and vocabulary comprehension age-equivalent scores for the controls. Narratives were elicited in 3 modes (oral, handwritten, and word-processed) using single-episode picture sequences. Narratives were analyzed for length, linguistic complexity, narrative structure, spelling, punctuation, and handwriting legibility. RESULTS: Analyses revealed significant group differences only for measures of narrative length (DS > TD) and handwriting legibility (TD > DS). Oral narratives were longer and more complex than written narratives for both groups. Regression analyses revealed that vocabulary comprehension was the best predictor of narrative skills for the group with DS; age was the best predictor of narrative skills for the TD group. CONCLUSIONS: These school-age students with DS exhibited many oral and written narrative abilities that were comparable with those of real-word-reading-matched controls. Several findings suggest a possible increased constraint of fine-motor skill in the DS group.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".