Financing Research Universities in Post-communist EHEA Countries
Bibliographic record
Abstract
The future of mankind depends largely on cultural, scientific and technical development; and that this is built up in centres of culture, knowledge and research as represented by true universities. National states have the necessity and obligation to guarantee the access to financial means for a healthy functioning of universities—even if this is not a direct state support. European tradition from the 18th century for financing universities was donation of properties to the institutions and direct state support. This tradition has changed from the last quarter of the 20th century on, due to a low level of financing HEIs. The situation is most dramatic in Eastern European post-communist EHEA countries, where properties were confiscated and state support is rather scarce due to the bad economic situation. Though research grants have been more or less available, their amount does not cope with the infrastructural necessities and the costs of human resources. As a result, university research in the region is much less competitive compared to the more advantageous (Western) universities. Documents of the European Research Area declared that the number of talented researchers should be the same regardless of the geographical situation, thus it is also the interest of ERA (and EHEA) to help support this handicapped region. A joint and concerted effort of national and European authorities is necessary to help the Eastern European post-communist EHEA countries to catch up with the intensity of university research and become real members of the European university community also in this respect.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".