Valorisation of knowledge: preliminary results on valorisation paths and obstacles in bringing university knowledge to market
Bibliographic record
Abstract
The current paper is concerned with exploring the outcomes of valorisation of technology focused research projects in the Netherlands. Drawing on an evaluation of 240 projects at universities in three cities and on in-depth knowledge of almost 50 projects, the paper explores to what extent technology inventions are brought to market and which factors hamper such development. An evaluation after 10 years indicates that a quarter of the projects could be brought to market and that in almost 30% of the projects research is still continuing. Looking back to factors hampering knowledge valorisation, it appears that shortage in the organizational situation at the university at that time is the most important factor. Problems in interaction with the business world are in second place The most important regional factor appears a shortage of financial incentives including easy access to (regional) venture capital. The implications of the results for policymaking and further research are discussed.
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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 | 0.029 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".