Knowledge and technology transfer from research organisations to businesses
Bibliographic record
Abstract
The development of an innovative economy depends on the ability to use the achievements of science and the possibility of their distribution. The book is part of an extremely important discussion on improving the innovativeness of the Polish economy by improving cooperation between the science and business sectors. The authors diagnose the situation of Polish universities in the field of commercialization of research results and cooperation with business, examine various groups of stakeholders participating in professional processes of knowledge and technology transfer in Poland, Norway, France, the Czech Republic, Hungary, as well as in the USA and Canada. They analyse studies of good practices - both Polish and foreign - in order to present recommendations for necessary changes for universities in the area of shaping good relations with enterprises in order to increase the innovation potential and increase the competitiveness of the economy. Originally published as Transfer wiedzy i technologii z organizacji naukowo-badawczych do przedsiębiorstw (University of Lodz, Poland, 2016).
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".