Content and Form in the Narratives of Children With Specific Language Impairment
Bibliographic record
Abstract
PURPOSE: This project investigated the relationship of content and form in the narratives of school-age children. METHOD: Two samples of children with specific language impairment (SLI) and their age-matched peers (British Columbia sample, M age = 9;0 [years;months], N = 26; Texas/Kansas sample, M age = 7;6, N = 40) completed the Test of Narrative Language (TNL; Gillam & Pearson, 2004). The relative strength of content elaboration and grammatical accuracy were measured for each child using variables derived from the TNL scoring system (Study 1) and from analysis of the story texts (Study 2). RESULTS: Both studies indicated that, compared with age peers, the children with SLI were more likely to produce stories of uneven strength--either stories with poor content that were grammatically quite accurate or stories with elaborated content that were less grammatical. CONCLUSIONS: These findings suggest that school-age children with SLI may struggle with the cumulative load of creating a story that is both elaborate and grammatical. They also show that the absence of errors is not necessarily a sign of strength. Finally, they underscore the value of comparing individual differences in multiple linguistic domains, including the elaboration of content, grammatical accuracy, and syntactic complexity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".