The Role of Foreign Language Learning as a Background of Tolerance and Cross-Ethnic Interaction Formation of Higher Educational Institutions’ Students
Bibliographic record
Abstract
The article deals with the role of language study in improving the level of tolerance and cross-ethnic interaction between students of higher educational institutions. The research methodology is based on the combination of general scientific and special methods that was ensured by the introduction of appropriate forms and methods of education, as well as the principles of objectivity, student-centricity, research and its results’ verification. The systemic analysis of normative acts, documents and international experience resulted in deeper understanding of basic notions of research and the importance of foreign language learning in the formation of tolerance and cross-ethnic interaction. At the initial stage of the experiment, low level of the tolerance formation and cross-ethnic interaction in the process of learning a foreign language was determined. This is due to inability to conduct intercultural dialogue, misunderstanding of ethnic and cultural differences in the process of communication. This prompted development and implementation the authors’ educational model, which provided realization of interactive forms and methods of learning as well as students’ involvement into socio-cultural educational environment. The outcomes of final empirical research highlighted positive dynamics of tolerance formation of higher educational institutions’ students when learning the discipline «Foreign Language for Professional Purposes».
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".