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Record W2936156849 · doi:10.59377/326j9846v

Using Existing Large-Scale Data to Study Early Care and Education among Hispanics: Project Overview and Methodology

2016· article· en· W2936156849 on OpenAlexfundno aff
Danielle A. Crosby, Julia Mendez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
FundersOffice of Planning, Research and EvaluationNational Safety Academic FundUniversity of North Carolina at GreensboroBowling Green State UniversityYork UniversityU.S. Department of Education
KeywordsScale (ratio)Data scienceRelevance (law)Computer scienceKey (lock)Knowledge baseGeographyPolitical scienceWorld Wide WebCartography

Abstract

fetched live from OpenAlex

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:

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.051
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.028
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.402
GPT teacher head0.490
Teacher spread0.088 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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