Understanding Mixed Emotions in Preschool: The Role of a Child’s Cognitive Development
Bibliographic record
Abstract
This paper aims to explore the relationship between preschool children’s understanding of mixed emotions and indicators of their cognitive development and gender and age. Mixed emotion comprehension is the ability of children to recognize and interpret emotions consisting of two emotions with different valences simultaneously. Assessment of preschool children’s understanding of mixed emotions was carried out using a set of tasks that modified Bylkina and Lucin’s methodology. Nonverbal intelligence was analyzed as indicators of cognitive development and children’s ability to apply dialectical thinking actions, perform formal operations, and predict the development of a situation. A total of 128 older preschool children took part in the study. The empirical study showed that understanding mixed emotions were related to the success of applying dialectical thought operations of transformation and mediation and formal operations of animation and prediction. No relationship was found between understanding mixed emotions and a child’s non-verbal intelligence. No differences were found in the success of understanding mixed emotions between girls and boys.
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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 | 0.002 | 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".