The Language Use Inventory for Young Children: A Parent-Report Measure of Pragmatic Language Development for 18- to 47-Month-Old Children
Bibliographic record
Abstract
PURPOSE: To demonstrate the internal reliability and discriminative validity of the Language Use Inventory for Young Children (LUI; D. K. O'Neill, 2002), a newly developed parent-report measure designed to assess pragmatic language development in 18-47-month-olds. METHOD: To examine internal reliability, the LUI was completed by mail by 177 parents recruited from the University of Waterloo's Centre for Child Studies database, 175 of whom completed the LUI again within 4 weeks to assess test-retest reliability. To examine discriminative validity, 49 parents of children awaiting assessment at a local speech-language clinic and 49 parents of typically developing children recruited from the Centre for Child Studies database and matched in age and sex to the clinic group completed the LUI. RESULTS: Alpha values for the subscales of the LUI were at or above acceptable levels (.80-.98), and steady growth in children's pragmatic language development was demonstrated. The study of discriminant validity revealed sensitivity and specificity levels over 95%. CONCLUSIONS: The LUI's internal reliability and stability were strongly supported and its sensitivity and specificity in distinguishing between typically developing and language-delayed children exceeded even the most stringent criteria of 90% accuracy.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".