Using Existing Large-Scale Data to Study Early Care and Education among Hispanics: Project Overview and Methodology
Notice bibliographique
Résumé
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:
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,051 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,007 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».