The Selection of ECEC Programs by Australian Families: Quality, Availability, Usage and Family Demographics
Bibliographic record
Abstract
HIGH-QUALITY EARLY CHILDHOOD education and care (ECEC) programs have the potential to ameliorate socioeconomic status (SES) gradients. In the Australian ECEC market, however, there is no guarantee that children from low SES backgrounds access high-quality ECEC programs. This study tested the influence of family SES on the selection of ECEC program quality. Participants were 2494 children enrolled in up to 1427 ECEC classrooms (mean age at entry = 43 months, SD = eight months). The study controlled for a range of child, family, home and community-level background factors. Both cross-sectional (linear regression) and longitudinal (growth models) methods are used. The study confirmed that children from lower SES families were more likely to attend lower quality programs. Longitudinal modelling showed the largest quality gap before kindergarten. To narrow SES-related achievement gaps there is a need to significantly improve aspects of program quality that influence children's development, and specifically to do so in programs for younger children. There is a particular need to target ECEC programs in lower SES areas to ameliorate the observed SES quality gradient. The findings further challenge current policy directions from the Productivity Commission inquiry into child care and early learning.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".