The role of emotion knowledge in the links between shyness and children's socio‐emotional functioning at preschool
Bibliographic record
Abstract
The present study investigated the potential protective role of components of emotion knowledge (i.e., emotion recognition, situation knowledge) in the links between young children's shyness and indices of socio-emotional functioning. Participants were n = 163 children (82 boys and 81 girls) aged 23-77 months (M = 53.29, SD = 14.48), recruited from preschools in Italy. Parents provided ratings of child shyness and teachers rated children's socio-emotional functioning at preschool (i.e., social competence, anxiety-withdrawal, peer rejection). Children were also interviewed to assess their abilities to recognize facial emotional expressions and identify situations that affect emotions. Among the results, shyness was positively related to anxiety-withdrawal and peer rejection. In addition, emotion recognition was found to significantly moderate the links between shyness and preschool socio-emotional functioning, appearing to serve a buffering role. For example, at lower levels of emotion recognition, shyness was positively associated with both anxiety-withdrawal and rejection by peers, but at higher levels of emotion recognition, these associations were attenuated. Results are discussed in terms of the protective role of emotion recognition in promoting shy children's positive socio-emotional functioning within the classroom context.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".