Models and determinants of vocabulary growth from kindergarten to adulthood
Bibliographic record
Abstract
BACKGROUND: Increasing evidence suggests that childhood language problems persist into early adulthood. Nevertheless, little is known about how individual and environmental characteristics influence the language growth of individuals identified with speech/language problems. METHOD: Individual growth curve models were utilised to examine how speech/language impairment and environmental variables (socioeconomic status, family separation, and maternal factors) influence vocabulary development from age 5 to 25. Participants were taken from a community sample of children initially diagnosed with speech/language problems at age 5 and their sex- and age-matched controls. RESULTS: The language impaired group had significantly poorer receptive vocabulary than the speech impaired and control groups throughout the 20-year period. Family income was a significant predictor of vocabulary growth when considered separately, but ceased to be a predictor when language impairment status was taken into account. Maternal education and family separation were determinants of vocabulary at age 5, over and above language impairment status. CONCLUSION: Language impairment is a significant risk factor for vocabulary development from childhood to adulthood. Individuals with speech impairment were less impaired on receptive vocabulary than individuals with language impairment. Further investigation into maternal and familial risk factors may provide targets for early intervention with children at risk for language impairment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".