Factors Affecting R&D Share in University Revenues: Case of Russia
Bibliographic record
Abstract
Recent government projects and initiatives (such as Priority 2030) have significantly increased the role of universities in creating and commercializing innovations in Russia. One of the most important indicators of university performance in these programs is R&D revenue. Its values have a significant deviation between universities that lead to different importance of R&D activities and make some universities more R&D oriented than others in the terms of their economics. This orientation can be considered an important factor of university sustainability as it allows it to be less dependent on admission volume which varies due to demographic waves and other endogenous factors. This paper studies the factors affecting the R&D orientation of big Russian universities. Monitoring the Efficiency of Higher Education Institutions provides sufficient data on Russian universities for such study including the share of R&D revenue in the total value of revenue which is used as a measure of R&D orientation. This study analyses the factors affecting this indicator using the data from the 49 largest Russian universities between 2015 and 2020 to build econometric panel data models. The modelling proves the significance of various factors such as entrance scores of students, the number of publications per faculty member, the share of young researchers, the ratio of average salary to the regional average salary, and the share of faculty members holding doctoral degrees. The research highlights the connection between publication performance and R&D activities and the importance of supporting young researchers in the development of scientific entrepreneurship.
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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 | 0.000 |
| Open science | 0.000 | 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".