Child Care Services, Socioeconomic Inequalities, and Academic Performance
Bibliographic record
Abstract
OBJECTIVE: To determine if child-care services (CCS) at a population level can reduce social inequalities in academic performance until early adolescence. METHODS: A 12-year population-based prospective cohort study of families with a newborn (n = 1269). Two CCS variables were estimated: "intensity" (low, moderate, and high number of hours) and "center-based CCS type" (early onset, late onset, and never exposed to center-based CCS). RESULTS: Children from low socioeconomic status (SES) families who received high-intensity CCS (any type), compared with those who received low-intensity CCS, had significantly better reading (standardized effect size [ES] = 0.37), writing (ES = 0.37), and mathematics (ES = 0.46) scores. Children from low-SES families who received center-based CCS, compared with those who never attended center care, had significantly better reading (ESearly onset = 0.68; ESlate onset = 0.37), writing (ESearly onset = 0.79), and mathematics (ESearly onset = 0.66; ESlate onset = 0.39) scores. Furthermore, early participation in center-based CCS eliminated the differences between children of low and adequate SES on all 3 examinations (ES = -0.01, 0.13, and -0.02 for reading, writing, and mathematics, respectively). These results were obtained while controlling for a wide range of child and family variables from birth to school entry. CONCLUSIONS: Child care services (any type) can reduce the social inequalities in academic performance up to early adolescence, while early participation in center-based CCS can eliminate this inequality. CCS use, especially early participation in center-based CCS, should be strongly encouraged for children growing up in a low-SES family.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".