Emotional Capital Within the Cultural Dimensions Framework
Bibliographic record
Abstract
Emotional capital (EC) has become an important concept in educational and intercultural communication. It is shown to be a booster capital potentializing human, social, and cultural capitals. The competencies comprising emotional capital are learned from the early ages through socialization process and get consistently reshaped in different contexts. Accordingly, the present study aims at exploring how cultural dimensions and emotional capital are related using Hofstede’s cultural framework. To this end, Emotional Capital Questionnaire was distributed among 180 students from Iran, the Unites States, China, Brazil, and India. The results revealed considerable cultural differences in the level of learners’ EC. Also, cultural specificity of emotional skills was confirmed using MANOVA. Further analyses have shown that cultures which emphasized maintenance of social order––that is, those with higher levels of masculinity and long-term orientation tended to have higher scores on EC. In contrast, in countries where people minimized the maintenance of social order and emphasized uncertainty avoidance tend to have lower scores on EC. In the end, practical implications of these findings are discussed, and future research directions are provided.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".