Startups and Spinoffs as Factors of the Academic Business Development: the Foreign Experience and the Ukrainian Prospects
Bibliographic record
Abstract
Introduction.Modern, equipped with cutting-edge technologies universities are a perfect environment for creating the primary atmosphere of innovative business climate.Problem statement.The implementation of modern, profitable innovation projects (startups or spinoffs) in the form of small innovative enterprises is a new way of successful commercialization of promising ideas.Universities are perfect site for launching the first startup ideas.Purpose.To study the trends of academic business development in the context of increasing importance of innovation activities, in particular, the role of startups and spinoffs in the activities of foreign and Ukrainian universities.materials and methods.There has been used the comparison of world universities ratings.The SWOT-analysis of the prospects of startup and spinoff projects has been carried out based on the universities of Ukraine.Analysis of dependence of the proceeds from innovative achievements on the costs of R&D works has been made. results.A comparative overview of the trends in the development of academic business abroad and in Ukraine has been made.The national and European legislation in the sphere of formation of R&D products has been analyzed.The factors that sufficiently hamper the development of innovative process in Ukraine have been identified.Recommendations concerning popularizing the innovative and investment activities and establishing fruitful international cooperation have been given.conclusions.The obtained results enable formulating the principles for the formation of a new methodological paradigm for intellectual assets management in universities taking into account the peculiarities of the development of national science, economy, entrepreneurship, and high-tech market.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".