The Challenge of Fundraising in Universities in Europe
Bibliographic record
Abstract
The current financial context constitutes a challenge to European Higher Education Institutions in the sense that they must look to increasing their budgets with new activities. This context has led governments and European universities to promote not only the traditional private funding sources (such as transfer of knowledge or tuition fees), but also new means of raising supplementary philanthropic (private) funds for educational and research purposes. Therefore, the actual European context may also represents an opportunity for the development of their fundraising strategy, since a determined fundraising program will bring supplementary private funds to the institutions reducing the frequent internal resistance and increasing the willingness to overcome the lack of experience in this field in Europe. This paper highlights the change and opportunity that raising independent income from donations implies, in order to achieve excellence and add value to core funding. Here we review UK and Spain cases with special attention in two case studies: Cambridge University, as an example of a well-known prestigious European university with extensive experience in fundraising; and the University of Navarra, as a case of a relevant experience in fundraising policy in a private southern European university.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.007 | 0.002 |
| 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".