Predicting Later Language Outcomes From the Language Use Inventory
Bibliographic record
Abstract
PURPOSE: To examine the predictive validity of the Language Use Inventory (LUI), a parent report of language use by children 18-47 months old (O'Neill, 2009). METHOD: 348 children whose parents had completed the LUI were reassessed at 5-6 years old with standardized, norm-referenced language measures and parent report of developmental history. The relationship between scores on the LUI and later measures was examined through correlation, binary classification, and receiver operating characteristic curve analysis. RESULTS: For children aged 24-47 months at the time of LUI completion, LUI scores correlated significantly with language measure scores. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated for 4 cutoff scores on the LUI, including -1.64 SD, a score that maximized sensitivity to 81% and specificity to 93%. For children aged 18-23 months at the time of LUI completion, specificity and NPV were high, but sensitivity and PPV were lower than desirable. CONCLUSIONS: The results provide initial support for the LUI's predictive validity, particularly for children 24-47 months, and suggest the LUI can serve as an indicator of later language outcomes in referred populations. The results compare favorably to findings for other early child-language measures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".