Early Childhood Education and Care Access for Children from Disadvantaged Backgrounds: Using a Framework to Guide Intervention
Bibliographic record
Abstract
Early childhood education and care (ECEC) can have substantial beneficial effects on overall child development and educational success for children from disadvantaged backgrounds. Unfortunately, it is well documented that they are underrepresented in quality ECEC programs. In order to improve access to quality ECEC, it is important to understand the factors leading to these inequities. This paper is based on a synthesis of published literature on interventions aimed at improving access to ECEC. We propose a framework identifying the spectrum of factors influencing access to quality ECEC for disadvantaged populations. We also present, in the context of our proposed framework, different interventions that have been taken to improve access to ECEC opportunities for children from low socioeconomic and/or new immigrant backgrounds. We believe that the framework proposed in this paper serves not only as a framework by which to understand the overlapping processes, factors, and stages affecting access to ECEC, but also as a model to help decision makers coordinate their efforts and maximize their impact towards more equity in access to quality early childhood education.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".