Differentiating School-Aged Children With and Without Language Impairment Using Tense and Grammaticality Measures From a Narrative Task
Bibliographic record
Abstract
PURPOSE: To determine the diagnostic accuracy of the finite verb morphology composite (FVMC), number of errors per C-unit (Errors/CU), and percent grammatical C-units (PGCUs) in differentiating school-aged children with language impairment (LI) and those with typical language development (TL). METHOD: Participants were 61 six-year-olds (50 TL, 11 LI) and 67 eight-year-olds (50 TL, 17 LI). Narrative samples were collected using a story-generation format. FVMC, Errors/CU, and PGCUs were computed from the samples. RESULTS: All of the three measures showed acceptable to good diagnostic accuracy at age 6, but only PGCUs showed acceptable diagnostic accuracy at age 8 when sensitivity, specificity, and likelihood ratios were considered. CONCLUSION: FVMC, Errors/CU, and PGCUs can all be used in combination with other tools to identify school-aged children with LI. However, FVMC and Errors/CU may be an appropriate diagnostic tool up to age 6. PGCUs, in contrast, may be a sensitive tool for identifying children with LI at least up to age 8 years.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".