Exploring the role of early childhood educators’ emotion socialization strategies in the development of young children’s social and non-social play behaviors
Bibliographic record
Abstract
It is widely postulated that caregivers’ emotion socialization strategies support children’s positive socio-emotional functioning with peers. However, this theoretical model has been rarely examined empirically in the context of early childhood education and care (ECEC), despite ECEC being a prominent environment for children to practice peer play (a robust marker variable for social and emotional competencies). This study explored the role of ECEC teachers’ emotion coaching and emotion distracting strategies in the development of children’s social and non-social play behaviors over time. Participants were 275 teachers and 487 children (aged 36–57 months) from 123 classrooms in 56 ECEC centers in Norway. Results from multilevel linear mixed modeling analyses indicated that emotion coaching was associated with a steeper increase in social play and steeper decrease in reticent behavior. In contrast, although emotion distracting was also associated with a steeper decrease in reticent behavior, it also predicted a less steep increase in social play. These results suggest that emotion coaching is a supportive socialization strategy for children’s peer relations in ECEC, whereas findings for distracting were more mixed. Using responses from multiple teachers within each classroom to examine both average classroom scores, in addition to minimum and maximum classroom scores, offers a novel insight into the group dynamics of teacher-child interactions in ECEC to support children’s peer relations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".