Too late and not enough for some children: early childhood education and care (ECEC) program usage patterns in the years before school in Australia
Bibliographic record
Abstract
This paper uses data from a major Australian longitudinal study to test the extent to which children recruited on the basis of attending an early childhood education and care (ECEC) setting when they were 3–4 years of age received an ‘optimal’ dosage of education and care. The idea of an optimal dosage is drawn from research literature on what level of dosage leads to improved learning and development outcomes for children. This dosage level is then compared with the actual level received by Australian children, through examining the age of entry of Australian children into ECEC and the number of hours of education and care they receive before school entry. Key predictors of the total hours of ECEC usage and the year of commencement in formal ECEC programs are reported, and demonstrate the variability and correlates of participation in ECEC programs. Patterns of ECEC usage were predicted by family advantage and disadvantage factors. Children from homes with less employment, and more siblings, tend to use fewer hours of ECEC before school and/or start later. The findings suggest sub-optimal levels of participation given the policy goal of improving learning and developmental outcomes for all children and particularly for children from disadvantaged backgrounds. Policy implications are addressed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".