The Preschool Entitlement: A Locally Adaptable Policy Instrument to Expand and Improve Preschool Education
Bibliographic record
Abstract
Only three out of five children are enrolled in preschool globally, and only one out of five in low-income countries, yet the expansion of preschool education came to a near standstill in 2020. To restart it, we propose a policy instrument called the Preschool Entitlement. It entails the right of every child to 600 hours of quality government-funded preschool education per year (3 hours per day, 5 days per week, 40 weeks per year). Existing preschool institutions and other organizations with legal status (public, private, faith- or community-based) can offer the child development program after a process of rigorous accreditation to ensure quality, inclusion, and safety. In other respects, they will have the freedom to shape the program according to local circumstances and local preferences. This makes it possible to supplement the daily 3 hours with additional hours of childcare that can be financed by families, local government, employers, national associations, faith-based organizations, ministries of social affairs, or others. In this manner, the Preschool Entitlement reconciles local autonomy with governmental responsibility for quality, access, and equity. In low- and middle-income countries, government costs would range from about 0.15 to 0.4 percent of GDP, and the benefits are likely to be significant.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".