English Language in the Development of a Tolerant Person of the Student in a Multi-Ethnic Educational Environment of the University (For Example, Kazan Federal University)
Bibliographic record
Abstract
Integration into the world community confronts the Russian education system purpose - education of the individual, having planetary thinking, able to see themselves not only as the representative of the native culture, living in a particular country, but also a citizen of the world, perceiving himself as the carrier and its foreign-language cultures. An important component of the socio-political development of multicultural education is the desire of both Russia and other countries integrate into the global European socio-cultural and educational space, while retaining their national identity. These processes contribute to the transformation of both Europe and Russia as a multilingual space where different languages have equal rights. There is an urgent social need for the organization of purposeful work on the formation of a tolerant person of the student, the system combines knowledge of different cultures, the desire and the willingness to intercultural Polylog. This era of social order urgently requires development of the younger generation of universal values, acculturation of other nations, which increases the motivation to learn foreign languages, associated with the desire to establish and develop contacts with foreign countries. It is English as a language of international communication acts as the major means to realize this idea in a multi-ethnic educational environment of the university. The article presents the results of a study conducted at the Kazan Federal University.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".