Quality of Early Childhood Education and Care: Examining Different Conceptualizations and Levels of Quality
Bibliographic record
Abstract
A large proportion of young children spend many hours in early childhood education and care (ECEC) and such care is costly. Given this context, it is important to examine the quality of care children receive, whether they experience quality differently, and the effects of quality on child development. Exposure to high quality ECEC is expected to promote child development. However, results linking ECEC quality to children’s outcomes are mixed. Perhaps researchers need to focus on different aspects of ECEC quality. Alternatively, limitations of the literature may explain this inconsistency, as assessments of ECEC quality often aggregate across individual children to the classroom level, and such research is conducted primarily in the U.S. My two dissertation papers address gaps regarding what impacts quality, children’s differential experiences of quality, and associations between quality and child outcomes. Both papers focus on classrooms serving preschool-aged children. The first paper is a systematic review and meta-analysis examining the role of educator early childhood specialization on a range of preschool-aged children’s outcomes. Findings from this paper indicate that this quality indicator is not strongly or consistently associated with the outcomes examined in the literature. Limitations of the literature are discussed as possible explanations for the few results. The second paper uses archival data to assess ECEC quality at the child, classroom, and centre levels, by examining whether and why children are differentially treated by ECEC educators. This paper consists of two studies. The first examines educator differential treatment (DT) of children using educator-reports of their perceived closeness and conflict with each child in their classroom. The second examines DT using child-educator observations of social interaction and instruction. Results indicate that individual children have very different experiences in ECEC settings across a range of affective, social, and instructional domains, regardless of how DT is assessed. Educators reported closer and less conflicted relationships with prosocial children and directed more positive support and engagement towards children demonstrating oppositional behaviour. In addition to child behaviour, aspects of classroom quality showed associations with DT, however, results were inconsistent. Implications for public policy, resource allocation, and future research, particularly in Canada, are discussed.
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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 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".