One size does not fit all: Associations between child characteristics, differential treatment of children by educators and quality in child care centers
Bibliographic record
Abstract
High-quality interactions in early childhood education settings support children's cognitive and socioemotional development. However, little is known about what explains variability in how educators interact with different children in these settings and how this variability relates to quality metrics. This study was based on data from 470 primarily low-income, preschool-aged children (mean age = 46.6 months; 53% female) attending licensed child care settings in a multicultural metropolis. Multilevel analyses revealed that approximately 80% of both observed educator behaviors and educator reports of relationship quality varied between children in the same classrooms, and that children's disruptive behavior, verbal intelligence, and hostility accounted for anywhere between 3 and 53% of this variance. Educators directed more positivity towards children who they described as more hostile and reported having closer and less conflictual relationships with children who they described as being less hostile and having greater verbal intelligence (small to moderate effect sizes). Differential treatment was associated with child-educator ratios, staff education, and emotional climate. Results can inform research, practice, and policy related to equity, professional development, and quality measurement in early childhood education.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".