Using Existing Large-Scale Data to Study Early Care and Education among Hispanics: Project Overview and Methodology
Bibliographic record
Abstract
Series overview and purposeIn communities across the United States, early care and education (ECE) settings serve as a key developmental context for children and critical work support for families.Given substantial evidence that high-quality ECE experiences can promote the healthy development of children and improve their short-and long-term outcomes, the federal government has invested in a range of ECE programs to help ensure that all children-regardless of income-can have access to these positive experiences.Increased funding for child care subsidies (e.g., the Child Care and Development Fund), Head Start/Early Head Start, and public pre-kindergarten in recent decades has greatly expanded ECE enrollment among children from low-income families.1 However, many eligible children still do not participate in these programs.Hispanic a children, in particular, are less likely than other groups to receive publicly supported ECE services.[2][3][4][5] Reasons for this vary, but include access barriers, family preferences and constraints, limited availability of affordable or quality programs, or some combination of these factors.6 Immigrant Latino families in particular may face additional language barriers, or they may be hesitant about involvement with public assistance programs because of safety concerns, if they have undocumented household members.7 It is imperative that Latino children be a central part of early childhood policy and research discussions.More than one quarter of all children age 5 and younger in the United States are Hispanic, and more than two thirds of these children live in poverty or near poverty (<200 percent of the federal poverty level).8 In order to better understand how Hispanic families perceive, access, and experience ECE, ongoing research is needed, with particular attention to the diversity that exists within the Latino population by nativity status, country of origin, language preferences, and other important characteristics.Secondary analyses of existing large-scale data sets provide a cost-effective and valuable way to contribute to this knowledge base about Latino populations.9 a In this brief series, we use the terms Hispanic and Latino interchangeably.Most of the large-scale surveys included in this review give respondents the option of identifying themselves (or their minor children) as being "of Spanish, Hispanic, or Latino origin." Why research on low-income Hispanic children and families matters Hispanic children currently make up roughly one in four of all children in the United States, a and by 2050 are projected to make up one in three, similar to the number of white children.b Given this, how Hispanic children fare will have a profound and increasing impact on the social and economic well-being of the country as a whole.Notably, though, 5.7 million Hispanic children, or one third of all Hispanic children in the United States, are in poverty, more than in any other racial/ethnic group.c Nearly two thirds of Hispanic children live in low-income families, defined as having incomes of less than two times the federal poverty level.d Despite their high levels of economic need, Hispanics, particularly those in immigrant families, have lower rates of participation in many government support programs when compared with other racial/ethnic minority groups.e-g High-quality, research-based information on the characteristics, experiences, and diversity of Hispanic children and families is needed to inform programs and policies supporting the sizable population of low-income Hispanic families and children.a Federal Interagency Forum on Child and Family Statistics.(2014).America's 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 | 0.051 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".