Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Public research is the source of many of todayâs technologies from the GPS and MRI to MP3 technology. Public research institutions (PRIs) and universities are also an engine of entrepreneurial ventures from biotech start-ups to Internet giants like Google. Today, globalisation, open innovation and new forms of venture financing such as crowd funding are changing the way institutions promote the transfer and commercialisation of public researcher results. This report describes recent trends in government and university level policies to enhance the transfer and exploitation of public research and benchmarks the patenting and licensing activities of PRIs and universities in a number of OECD countries and regions, including the EU, Australia, Canada, and the US. Finally, it also showcases, based on case studies of leading institutions in Finland (Aalto Center for Entrepreneurship), Germany (Fraunhofer Institute), the Czech Republic (Technology Transfer Office of the Czech Technical University), Japan (open innovation in firms), United States (National Institutes of Health) a number of good practices for increasing the number of university invention disclosures, accelerate licensing contracts and promote more open innovation practices between universities and firms. Â
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.
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 | 0.004 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it