Differentiating School-Aged Children With and Without Language Impairment Using Tense and Grammaticality Measures From a Narrative Task
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it