Cross-cultural comparison of trait emotional intelligence: A study of Kazakh and Canadian children
Bibliographic record
Abstract
The concept of Trait emotional intelligence (TEI) continues to attract interest from researchers across various fields. While the literature emphasizes the importance of TEI throughout the lifespan, the question of whether the TEI construct generalizes across diverse cultural groups remains largely unaddressed in children. The present study examined the generalizability of the youth version of a widely used self-report measure of TEI (EQi:YV-Short) in Canadian and Kazakh children. Families in Kazakhstan offer a useful comparison to those in Canada, as this Asian culture traditionally emphasizes social interdependence- a very different emotion socialization context from the emphasis on independence in Canadian families. A sample of 200 children (92 boys and 108 girls) between the ages of 9 and 11 from Kazakhstan were compared to a sample of Canadian children ( n = 200). The mean age of the boys in both samples was 9.45 years (SD = 0.52) and for girls was 9.41 years (SD = 0.55). The Canadian sample was collected from children in central and eastern Ontario who identified themselves as “white/Caucasian” and were randomly matched with the Kazakh sample based on age and gender. The results show that Kazakh children scored higher on the intrapersonal dimension, while Canadian children scored higher on the interpersonal, stress management and total TEI dimensions. Findings are discussed with reference to cross-cultural generalizability and applications of TEI theory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".