Language Personality in the Conditions of Cross-Cultural Communication: Case-Study Experience
Bibliographic record
Abstract
The article is devoted to the problem of identification of a language personality’s traits under conditions of cross-cultural communication. It is shown that effective cross-cultural communication is revised under globalization and increasingly intensive social interactions. The results of the authors’ research prove that it is possible to develop a new perspective on the heuristic possibilities of the concept of language personality to ensure the effectiveness of cross-cultural communications.This applies above all to the understanding of culture, cultural codes, verbal, non-verbal communication and preverbal, development of value measurement and understanding, and behavior adoption patterns. We propose to identify a language personality as a nationally specific communicant type that has a culturally caused worldview and value system and is capable of cross-cultural transformation. We identified transitions from a “mono” language personality to a “multi” language personality. We offer communicative training as a way of resolving cultural gaps in communication.We insist that only a new type of a language personality can effectively integrate and communicate while taking into account cultural peculiarities. Language personality currently acquires multicultural traits resulting from two main types of mobility: virtual and physical. Empirical research shows that two types of mobility are widespread, with typical high demands for the study of an international communication language (English) and local culture (Hebrew).
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".