Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it