Associations between Directors’ Characteristics, Supervision Practices and Quality of Early Childhood Education and Care Classrooms
Bibliographic record
Abstract
Research Findings: We investigated associations between the characteristics of directors, their practices in supervising educators, and the quality of classrooms in their centers. Directors from 71 randomly selected child care centers (106 classrooms) serving preschool-age children in Toronto, Canada, completed a questionnaire asking about their characteristics (e.g., education/experience) and supervision practices. Quality was assessed using the Classroom Assessment Scoring System (CLASS) and a short version of the Early Childhood Environment Rating Scale-Revised (ECERS-R). Most directors were female and had a strong early childhood education background. Their characteristics showed no, or negative, associations with supervision practices. Variance decomposition analysis revealed significant center level variance for the Emotional Support and Classroom Organization subscales of the CLASS and for the ECERS-R score. Experience in the current center, in the early childhood sector and administrative experience were negatively related to ECERS-R scores. No associations were observed with the CLASS. Practice and Policy: While our findings show center level effects for quality, they highlight the need for further research on whether and how directors drive center 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".