Bibliographic record
Abstract
The author documents pervasive racial disparities in the health of American children and analyzes how and how much those disparities contribute to racial gaps in school readiness. She explores a broad sample of health problems common to U.S. children, such as attention deficit hyperactivity disorder, asthma, and lead poisoning, as well as maternal health problems and health-related behaviors that affect children's behavioral and cognitive readiness for school. If a health problem is to affect the readiness gap, it must affect many children, it must be linked to academic performance or behavior problems, and it must show a racial disparity either in its prevalence or in its effects. The author focuses not only on the black-white gap in health status but also on the poor-nonpoor gap because black children tend to be poorer than white children. The health conditions Currie considers seriously impair cognitive skills and behavior in individual children. But most explain little of the overall racial gap in school readiness. Still, the cumulative effect of health differentials summed over all conditions is significant. Currie's rough calculation is that racial differences in health conditions and in maternal health and behaviors together may account for as much as a quarter of the racial gap in school readiness. Currie scrutinizes several policy steps to lessen racial and socioeconomic disparities in children's health and to begin to close the readiness gap. Increasing poor children's eligibility for Medicaid and state child health insurance is unlikely to be effective because most poor children are already eligible for public insurance. The problem is that many are not enrolled. Even increasing enrollment may not work: socioeconomic disparities in health persist in Canada and the United Kingdom despite universal public health insurance. The author finds more promise in strengthening early childhood programs with a built-in health component, like Head Start; family-based services and home visiting programs; and WIC, the federal nutrition program for women, infants, and small children. In all three, trained staff can help parents get ongoing care for their children.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".