Bibliographic record
Abstract
According to Aghion et al. (2007; 2008; 2010), the research performance of universities is positively correlated to the amount of budget per student. Italian universities usually perform poorly in international rankings, while the English university system is widely recognized as one of the best in the world. In this paper, Italian university funding is discussed by comparing data from the consolidated financial statements of English and Italian universities. The main results indicate that English universities are on average much better funded than Italian universities, from both public and private sources, but there are no noticeable differences in the amount of block grants per FTE student received by the State (FFO and Recurrent Grants, data 2007/2008). There are differences, however, in the share of the public block grant distributed competitively after an independent quality assessment of research activity. In England, this share amounts on average to roughly one quarter of the grant. In Italy, a quality assessment has recently been introduced but it involves only 7% of the grant (2009 data). The gap in the total available resources between the two university systems is due to two other main sources: student fees and research funding. The gap in research funding is both quantitative and due to the way in which resources are allocated. In England, research funds amount to 47% of the block grant, while in Italy the share is only 21%. In particular, the amount of public funds available for research is 12.5 times higher than in Italy, a huge difference. More importantly, two-thirds of total research funds are awarded through peer review and open competition, compared to Italy’s one-fourth. The lack of funds and the lack of incentive effects due to the limited role of open and competitive allocation of resources can explain the poor research performance of the Italian university system.
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 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 | 0.002 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".