Populations Served by Child Care Centers Accepting Subsidies and Linkages with State Subsidy Policies
Bibliographic record
Abstract
Research Findings: We conducted a latent profile analysis using data from centers (n = 3,474) employing Child Care and Development Fund (CCDF) subsidies in the 2019 National Survey of Early Care and Education. We identified subgroups of centers based on enrollment of children from CCDF priority populations and other diverse backgrounds. There were three subgroups: centers offering a wider breadth of services for priority populations, centers responsive to specific child or family needs, and centers with less emphasis on priority populations. While CCDF priority populations are represented across child care centers nationally, we found some disparities in populations served by different groups of centers related to children’s race/ethnicity and disability status. We also used multinomial logistic regression with state policies from the CCDF Policies Database. State CCDF policies predicted differences between groups of centers. Practice or Policy: Child care subsidies can improve families’ child care participation by reducing their costs. Because CCDF subsidies are not usually available for all eligible families, federal law prioritizes specific groups of children/families to promote more equitable ECE access. Our findings suggest that states may have opportunities to improve equitable access to child care by considering how priority populations align with demonstrated needs.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".