Shifting geopolitics of the European higher education space
Bibliographic record
Abstract
Given recent major geopolitical events in the European region, such as Brexit and the launch of a full-scale invasion of Ukraine, this article explores recent and under-researched shifts in the geopolitics of the European higher education space, focusing specifically on the European Higher Education Area (EHEA). The analysis is informed by the critical geopolitics approach and relies on interviews with stakeholders in the four countries that established the EHEA (Germany, France, Italy and the UK), key recent EHEA official communications, and a thematic analysis of these datasets. The analysis has revealed three major recent overlapping shifts in EHEA geopolitics: regarding its borders, identity and values. These findings are significant, highlighting the existence and dynamics of the phenomenon of higher education geopolitics in Europe and addressing an under-researched area in the literature on European geopolitics and the role of higher education in it.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 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".