University‐to‐industry knowledge transfer: literature review and unanswered questions
Bibliographic record
Abstract
This paper reviews the economic literature concerning university‐to‐industry knowledge transfer. Papers on this topic are divided into four categories. Research in the ‘firm characteristics’ category focuses directly on company issues, such as internal organization, resource allocation, and partnerships. In contrast, research in the ‘university characteristics’ stream pays little attention to the firms that commercialize inventions, but rather focuses on issues relating to the university, such as licensing strategies, incentives for professors to patent, and policies such as taking equity in return for intellectual property. The ‘geography in terms of localized spillovers’ stream of research considers the spatial relationship between firms and universities relative to performance in terms of knowledge transfer success. Finally, the ‘channels of knowledge transfer‘ literature examines the relative importance of various transfer pathways between universities and firms, such as publications, patents, and consulting. Each of these research streams is discussed and key papers are described highlighting important methodologies and results. Finally, an outline of topics requiring further research in each of the four categories is offered.
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".