Early development of emerging and English-proficient bilingual children at school entry in an Australian population cohort
Bibliographic record
Abstract
Children who enter school with limited proficiency in the language of instruction face a range of challenges in negotiating this new context, yet limited data have been available to describe the early developmental outcomes of this subpopulation in the Australian context. The Australian Early Development Index (AEDI) is a teacher-rated checklist that measures five important domains of child development: physical health and wellbeing, social competence, emotional maturity, language and cognitive skills, and communication skills and general knowledge. In 2009, the AEDI was completed for 97.5% of Australian children in their first year of schooling ( N = 261,147; M = 5 years, 7 months of age), providing a unique opportunity to explore the cross-sectional associations between language background, proficiency in English, and early developmental outcomes at the population-level. Logistic regression analyses revealed that, compared to their peers from English-speaking backgrounds, bilingual children who were not yet proficient in English had substantially higher odds of being in the “vulnerable” range (bottom 10th percentile) on the AEDI domains ( OR = 2.88, p < .001, to OR = 7.49, p < .001), whereas English-proficient bilingual children had equal or slightly lower odds ( OR = .84, p < .001, to OR = .97, ns). Future research with longitudinal data is now needed to establish causal pathways and explore long term outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".