Measuring Quality in Two Early Childhood Education Contexts: Centre-Based Childcare and Four-Year-Old Preschool
Bibliographic record
Abstract
The quality of early childhood experiences is crucial to a child’s development and educational success. Yet few early childhood education and care services in the world today offer a consistently high level of educational quality. In particular, educational quality depends on the context’s characteristics. The aim of this study was therefore to measure and compare the educational quality experienced in two distinct educational contexts, located in Quebec: early childhood centres and 4-year-old preschools. Results of the study indicate that there are very few significant differences between these two educational contexts in terms of interaction quality and pedagogical orientations quality, while variables related to structural quality vary greatly. Correlational and regression analyses carried out separately on each educational context show that few variables are predictive of interaction quality levels, suggesting that other variables, notably related to pedagogical orientations quality, would better explain variations in adult-child interactions predictive of child development. These results have implications for initial training curriculum aimed towards adults working in early childcare and for future directions in research on educational quality, including rethinking the importance of pedagogical orientations and structures in the ecosystemic model of quality.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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 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".