Developmental Trajectories of Negative Peer Play in Preschool: Predictors and Outcomes
Bibliographic record
Abstract
This study aims to identify age-related trajectories of preschoolers’ negative peer play, their predictors, and school-related outcomes. The participants were 1,547 children in the Panel Study on Korean Children and their teachers and parents. Using latent class growth analysis, we identified negative peer play trajectories of children between 4 and 6 years old. Analyses of variances were conducted to investigate whether children’s school readiness at 6 years old differed between the trajectories. Finally, multinomial logistic regressions were conducted to explore how teacher-child interactions influenced membership in the trajectories. Three developmental trajectories of play disruption were found: “Low” (64.3%), “Constant-higher” (34.3%), and “U-curve” (1.4%). In the case of play disconnection, four trajectories were found: “Low-increase” (57.6%), “Moderate-decrease” (26.5%), “Sharp-increase” (10.1%), and “High-decrease” (5.8%). The trajectories of play disruption were related to social and emotional development and approach to learning. The trajectories of play disconnection were related to all aspects of school readiness including social and emotional development, approach to learning, communication, and cognitive development. Teacher-child interactions that encourage children’s prosocial behaviors and positive peer interactions predicted likely membership in “Low-increase” play disconnection development. Also, teachers’ affectionate and sensitive qualities during the interaction with children predicted a “Low” trajectory of play disruption. Together, the results emphasized the protective power of positive teacher-child interactions in the development of preschool negative peer play. Based on the findings, policy implications are discussed with regard to teacher 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".