Emotion Recognition in Different Cultures
Bibliographic record
Abstract
Background: This article substantiates the necessity to study the emotion recognition in cross-cultural communication. The study is aimed at determining the conditions of successful cross-cultural recognition of face expressions. Methods: The authors have theoretically analyzed and summarized the conditions affecting the success of emotion recognition. To achieve the research objectives the authors used such methods as: the Cultural Intelligence Scale Test (CQS) by Early and Ang, Montreal Set of facial displays of emotion by U. Hess, the Embedded Figures Test of field dependence-independence by H. Witkin, Individualism and collectivism scale by G. Hofstede, Emotional intelligence Test by D.V. Lyusin. Findings: It was found that a high level of emotional intelligence, as well as the high level of its components, is closely related to the emotion recognition from facial expressions displayed by representatives of different cultures. Difficulties in emotion recognition are not determined by the fact that they appear quite different on the faces of the representatives of different ethnic groups, nationalities, cultures, etc. Difficulties in emotion recognition are related to the characteristics of its display and perception determined by the features of ethnic groups, person’s culture-specific and cognitive style features. Improvements: The authors formulated practical recommendations for the emotion recognition abilities development: emotional intelligence and its components; cognitive component of cultural intelligence; awareness of cultural differences in emotion expression and recognition. Keywords: Basic Emotions, Cognitive Features, Cross-Cultural Features, Emotion Recognition
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".