Early Childhood Education and Child Development Outcomes in Least Developed Countries: Empirical Evidence from Lao PDR
Bibliographic record
Abstract
Given the benefits of early childhood education, many countries try to ensure universal accessibilityto early childhood education. However, with their limited budgets and chronic poverty, least developed countries face a huge disadvantage in providing access to early childhood education, especially for children of lower income families and those living in remote areas. This study aims to determine how accessibility to early childhood education and child development affects cognitive, learning, physical, and social-emotional readiness.We use nationally representative data from the Lao Social Indicator Survey (LSIS) for a case study of Lao PDR, which is representative of least-developed countries. Our estimation indicates that mother’s educational attainment and economic status of the family have an important impact on children’s preschool enrollment. In terms of children’s development, receiving early childhood education is likely to play a significant role in developing cognitive skills. Furthermore, in addition to early childhood education per se, activities associated such education also play an important role in fostering children’s development. Hence, early childhood education should be promoted in order to enhance all children’s access to preschools and thus ensure that their development remains on track.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".