Language Issue in German Higher Education Internationalization: Ideologies, Management and Practices for English-Medium Instruction
Bibliographic record
Abstract
Employing document analysis and corpus-assisted discourse analysis, this study examines the language ideologies in German higher education internationalization policies and strategies, across European, German federal and university levels. It further investigates how these policies and strategies relate to the English-medium instruction practices in German universities, adopting Spolsky’s (2004, 2009) framework for language policy analysis. Results show that all institutions at the three levels recognize the role of EMI in promoting the higher education internationalization, but their policy documents adopt evasive attitudes towards EMI to varying degrees. The internationalization policies at the European level show the most tendency to evade the language issue, especially English, while the German federal internationalization documents include more contents about language, even EMI, and they are concerned about promoting the German language alongside English. At the university level, the welcoming attitudes towards EMI are displayed most overtly in the internationalization strategies and the rapid development trend and the predominant English-only type of EMI programs. The study underscores the potential benefits of a multilayered analysis of higher education internationalization policies and EMI practices, and the universities’ important role in balancing the English and the national language in the higher education domain.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.000 | 0.003 |
| 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".