Toward a hybrid model of quality for Quebec early childhood education: A structured group approach with diverse stakeholders
Bibliographic record
Abstract
Although extensive research has demonstrated the benefits of high-quality early childhood education, established quality frameworks often overlook the perspectives of stakeholders who are central to local improvement. Leveraging Quebec, Canada’s tradition of exemplary early childhood education (ECE), this study explored the concept of ECE quality with multiple stakeholder groups across the province, including parents, educators, administrators, and researchers. A structured group concept-mapping process identified seven components that participants saw as important for excellence in center-based early childhood education, including ideas not present in established quality frameworks. Participants viewed an exemplary ECE center as one that (1) offers children choices and stimulating learning experiences based on exploration and play, (2) adopts best practices in ECE, (3) ensures that staff have developmentally sensitive knowledge and skills, (4) provides professional support and good working conditions for staff, (5) partners with families and the community, (6) values diversity and inclusion, and (7) offers a stimulating physical and nutritional environment. These results suggest a Quebec-specific model of ECE quality spanning multiple ecological levels that reflects widely accepted scientific concepts as well as locally relevant ideas emerging from stakeholders’ perspectives. In addition to suggesting components for a Quebec-specific, stakeholder-driven model of quality, the research described in this article offers ideas and methods for similar initiatives in other contexts.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".