New Challenges of Anglicisation in the Context of 21st Century Globalization and Their Impact on Higher Education
Bibliographic record
Abstract
The paper considers the phenomenon of anglicisation to be predetermined: a) by the multilateral influence of the USA on many countries; b) by the obvious need of unification and standardization of terminological and other layers of lexis referring to various aspects of human activities, and c) by the prestigious role of English as a lingua franca in international cooperation among countries. Attitudes differ towards the process of anglicisation. While some appreciate its political, economic and cultural advantages, others warn against a global, dominating language that might absorb minor cultures. The process of anglicisation has its impact on higher education. One of the challenges faced by universities is to prepare students for effective intercultural communication in order to succeed in a global work environment. Therefore, the purpose of the present work is, first, to analyse the debates concerning the present and future perspectives of the process of anglicisation; second, to overview critically the research related to the integrated teaching of language and culture promoting three-dimensional linguo-cultural methodology of such teaching which facilitates to raise L2 students’ critical cultural awareness, and enables them to develop the essential communicative skills that are necessary for successful international collaboration.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".