The formation of students’ national self-awareness in EFL class
Bibliographic record
Abstract
© Canadian Center of Science and Education. In the epoch of globalization it is urgently important to draw attention to the problem of the formation of national self-awareness of school students. Numerous researches in the Russian Federation show that there is a tendency of cultural level decreasing, according to which a great many school students are not aware not only of the world's cultural heritage, but have a vague idea of their own (national) culture. The authors examine the role of foreign languages learning in the formation of national self-awareness of students in High schools. Language, as it is, reflects the culture and history of the nation. Therefore it can be declared a unique source in the process of national self-awareness formation of school students. The cross-cultural approach and the cross-cultural comparative approach in teaching EFL let both, teachers and students, consider human problems from the standpoint of two or more cultures that contribute to the comparative-contrastive analysis of native culture phenomena and compare the native culture with the culture of the target language. Drawing on literature of teaching and learning EFL, multicultural studies, as well as personal international teaching and learning experience, the authors examine the teaching and learning techniques focused on these approaches and present the authentic model of the formation of students’ national self-awareness based on the integration of teaching the language and local lore in EFL classes. The paper discusses issues and practices of the studied model and offers general recommendations for High schools faculty.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".