Cross-Cultural Competence of Personality and Adaptation to Polycultural Environment: Approaches and Techniques
Bibliographic record
Abstract
The article considers the cross-cultural competence of an individual as an important factor influencing the success of adaptation of various categories of people immigrants, refugees and internally displaced persons, returnees, representatives of ethnic and cultural minorities, as well as representatives of multicultural societies.Based on foreign studies, different approaches to the development of practical programs aimed at increasing intercultural competence and the effectiveness of socio-cultural adaptation are analyzed.In many cases, the fate and career of a person depends on successful intercultural interaction.Cultural differences become a particularly important factor when a person is forced or voluntarily in another sociocultural environment, where he must deal with various life and business tasks.Ignorance of cultural values, roles, norms and rules of conduct can be a major obstacle in international political and business negotiations, on the way to survival in a new culture and achievement of goals and education.In addition to knowledge of another culture, the skills and abilities that will determine the realization of this knowledge, as well as the desire to overcome all obstacles and to succeed in activities in intercultural interaction are necessary.The combination of knowledge, personality traits, motives and specific skills that contribute to the effective achievement of goals in intercultural interaction, was called intercultural competence.This complex theoretical construct, extremely needed in practice, has attracted the attention of a huge number of researchers and developers of practical programs.Thus, the approaches discussed give a broad opportunity to choose methods and technologies for solving problems that arise in intercultural interaction.Despite the general tendency to develop complex methods, the specific approaches, as shown above, continue to exist and develop, taking into account the latest advances in this field.Traditional methods, such as lectures, case studies, role-playing games, game simulators, the method DOI 10.32370/IA_2019_12_25 of critical incidents are enriched with video materials, the use of computer self-diagnosis, interactive video and online learning.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".