Культурно-интеллектуальные особенности распознавания лицевой экспрессии представителей других этносов
Bibliographic record
Abstract
Introduction. The paper examines the cultural and intellectual features of facial expression recognition by different ethnofors. These features are the following: (a) the experience of communication; (b) the experience of perceiving faces and facial expression; (c) knowledge of the rules of emotion expression and cultural styles of emotion expression. The study presents domestic and foreign approaches to perceiving faces and facial expression recognition. The novelty of the research lies in studying personal cultural and intellectual abilities during adaptation to living in a multicultural space of modern society through facial expression recognition when interacting with other ethnofors.
 Materials and Methods. The study employed the Cultural Intelligence Scale to study personal cultural and intellectual abilities. The Montreal Set of Facial Displays of Emotion (MSFDE) was the technique to study the ability to recognize facial expressions of representatives of their own and other ethnocultural groups. The participants comprised 129 Russian students and 129 students of Asian origin (from China, Vietnam, and Mongolia). The average age of research participants was 24 years.
 Results. The respondents recognized facial expressions of Asians better due to a metacognitive component, while a motivational component allowed them to better recognize facial expressions in whole. This was the result of the desire to understand another ethnofor.
 Discussion. Russian respondents’ greater openness and communicative experience determined their success in facial expression recognition. Asian respondents were more closed and less engaged in interethnic interaction. A person with high metacognitive intelligence abilities is more successful in recognizing facial expression, because he/she consciously chooses a strategy of intercultural interaction. High motivational and intellectual abilities are advantages in recognizing facial expressions of representatives of different cultures, because they are responsible for the desire to understand another culture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.017 |
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; both teacher heads agree on what is shown here.
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".