What type of enterprise forges close links with universities and government labs? Evidence from CIS 2
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
Abstract This paper tries to uncover some of the economic factors that encourage firms to seek information from universities and government labs or to collaborate with these institutions. We exploit the information contained in the second Community Innovation Surveys (CIS2) for France, Germany, Ireland and Spain. We estimate an ordered probit model on the importance of knowledge sourcing from universities and government labs controlling for selection bias, and a trivariate probit model explaining the decisions to innovate, collaborate in innovation, and in particular collaborate with universities and government labs. R&D‐intensive firms and radical innovators tend to source knowledge from universities and government labs but not to cooperate with them directly. Outright collaborations in innovation with universities and government labs is characteristic of large firms, firms that patent or those that receive government support for innovation. Members of an enterprise group tend to cooperate in innovation but not directly with universities or government labs. Copyright © 2003 John Wiley & Sons, Ltd.
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 | 0.000 | 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 | 0.001 |
| 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 it