A New Intercultural Model for Teaching Russian as a Foreign Language at European Level
Bibliographic record
Abstract
This paper presents a new experimental intercultural theoretical and operative model for RFL (Russian as a Foreign Language) teaching at European level: the RETEACH model. As a descriptive–normative didactic model, RETEACH builds on the intercultural approach, seeking to foster the development of intercultural communicative competence (ICC) among students. RETEACH promotes respect, understanding, and solidarity among individuals and encourages critical awareness of various issues, such as multiple identities, fuzzy cultural borders, power-related intercultural dynamics, and avoidance of stereotyped representations. The model is grounded in three specific concepts and areas within the foreign language and RFL fields: 1) the use of authentic texts, 2) the role of literature, and 3) textbook theory. The model was tested through two case studies, which investigated the place of culture in RFL classes in Lithuania and Italy (Case Study 1) and in RFL textbooks employed in Italy (Case Study 2). Three different research methods were employed: action research, classroom observation, and comparative content analysis. The findings indicate that the proposed model can boost the development of ICC in RFL learners, thus addressing some general issues with RFL teaching in the European context. There are several theoretical and practical implications of the RETEACH model: 1) combining RFL theory and practice, 2) improving the intercultural approach due to the use of authentic materials, 3) enabling the use of Russian literature to develop ICC, and 4) promoting a complex and critical image of Russia.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".