Rethinking Business Models for 21st Century Higher Education: A European Perspective
Bibliographic record
Abstract
The late 20th century was an era of social, economic, technological, and political change, resulting in significant shifts in the perception of enlightenment, knowledge, and education. The impact of these changes have become quite apparent in higher education where there is now mounting pressure for faculty to deliver high quality education to an internationally mobile cohort and where institutions are striving to attract funding, researchers, research grants, top students, and teaching staff. To cope with the many challenges, new business models are needed. Introducing change, however, is fraught with many problems; in particular, institutional barriers among disciplines, management commitment, socio-economic factors, and cultural issues. In this paper, we take a look at and discuss three European higher education institutions currently undergoing transformation—a British, a Finnish, and a Russian—to draw attention to some of the inherent factors that higher education institutions face when they seek to implement new business models to manage the competitive environment for higher education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".