Predicting Language Outcomes at 4 Years of Age: Findings From Early Language in Victoria Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To quantify the contributions of child, family, and environmental predictors to language ability at 4 years. METHODS: A longitudinal study was performed with a sample of 1910 infants recruited at 8 months in Melbourne, Australia. Predictors were child gender, prematurity, birth weight and order, multiple birth, socioeconomic status, maternal mental health, vocabulary, education, and age at child's birth, non-English-speaking background, and family history of speech/language difficulties. Outcomes were Clinical Evaluation of Language Fundamentals-Preschool, language scores, low language status (scores >1.25 SDs below the mean), and specific language impairment (SLI) (scores >1.25 SDs below the mean for children with normal nonverbal performance). RESULTS: A total of 1596 children provided outcome data. Twelve baseline predictors explained 18.9% and 20.9% of the variation in receptive and expressive scores, respectively, increasing to 23.6% and 30.4% with the addition of late talking status at age 2. A total of 20.6% of children (324 of 1573 children) met the criteria for low language status and 17.2% (251 of 1462 children) for SLI. Family history of speech/language problems and low maternal education levels and socioeconomic status predicted adverse language outcomes. The combined predictors discriminated only moderately between children with and without low language levels or SLIs (area under the curve: 0.72-0.76); this improved with the addition of late talking status (area under the curve: 0.78-0.84). CONCLUSIONS: Measures of social disadvantage helped explain more variation in outcomes at 4 years than at 2 years, but ability to predict low language status and SLI status remained limited.
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.
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 | 0.000 | 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) | 0.001 | 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 it