Content of Building Intercultural Competence in Future Specialists of Combat and Operational Support
Bibliographic record
Abstract
The article deals with a crucial research issue of acquiring intercultural competence by future specialists of combat and operational support.Following the recent researches intercultural competence of future specialists of combat and operational support is interpreted as an ability to cooperate with professionalsrepresentatives of other cultures with regard for their values, norms, traditions and images and also choose the expedient ways of verbal and nonverbal communication with the aim to solve professional problems.The components of intercultural competence of future specialists of combat and operational support include knowledge (language, speech, multicultural knowledge, knowledge of verbal and nonverbal communicative rules of behavior, cognitive knowledge), skills and abilities (speech skills and abilities, cognitive skills, an ability to control own actions in communication).While studying disciplines ''Foreign language'', ''Foreign language for Specific Purposes'' students acquire basic elements of foreign language system, develop and improve speech knowledge, skills and abilities.Introduction into third year students' syllabus the discipline ''Intercultural Communication in Professional Cooperation'' and organization of learning and field training envisages implementation of intercultural competence while performing professional duties and functions. Key word: intercultural competence,
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".