Can universal preschool education intensities counterbalance parental socioeconomic gradients? Repeated international evidence from Fourth graders skills achievement
Bibliographic record
Abstract
This study estimates the average multivalued treatment effects (ATET), of preschool attendance measured in years, on students' international reading, math and science test z-scores in Grade 4. The causal treatment effects come from multiple-years observational data on three levels of preschool duration before entering Grade 1. Among European countries that participated in five international education surveys, PIRLS (2006, 2011, 2016) and TIMSS (2015, 2019), those renowned for having adopted early childhood education (ECE) programs starting at a young age, growing in intensity and improving the number of qualified child-care providers were selected. In addition to four Nordic countries (Denmark, Finland, Norway, Sweden), France, two Belgium jurisdictions (French, Flemish), and two participating Canadian provinces, Ontario and Québec, were retained. The approach exploits the repeated surveys and cross-national comparative international z-scores tests. The data sets besides their test scores provide unique information from a parent questionnaire on their education and occupation levels, literacy and numeracy preschool activities, on child preschool educational childcare span in years and two program types (for some years; before and after age 3). Four key findings can be identified from the data sets and estimations. First, there are large differences in the average scale score and percentiles deviation when converted into the z-score metric, for all categories of test scores across jurisdiction participants, and over time. Second, the estimates of the preschool treatment effects display rather heterogeneous impacts on z-scores with increasing significant and positive achievements over year surveys. Third, in general, preschool treatment effects are scattered in function of duration, programs types, and parental education. Four, results highlight stark gaps in scores related to parental education, socioeconomic statuses, and home learning resources for all year-samples. Evidence from a diversity of estimated gradients suggests established social inequalities in education achievement at ages 9-10 in Grade 4 could be difficult to reverse, even in cases where preschool education and care are implemented at a very young age in rich countries with very generous family policies.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".