Early development of emerging and English-proficient bilingual children at school entry in an Australian population cohort
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
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.
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 | 0.001 | 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 | 0.001 |
| 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