Academic entrepreneurs and technology transfer: who participates and why?
Bibliographic record
Abstract
Introduction According to Schumpeter, innovation is about entrepreneurship: the implementation of new ideas that change established procedures and alter organizational practices (1934). While the idea of creative destruction is compelling, there are few opportunities to observe the characteristics of change agents in situations where new practices emerge. University technology transfer – the realization of commercial value from university research – presents such an opportunity. While universities are an important source of invention and new knowledge, there is great variation across universities in the commercial realization of academic discoveries (Nelson, 2001). This result is understandable when we consider that university technology transfer has only become a formal activity for most universities in the United States in the last twenty-five years. In this regard, a series of changes marked by the passage of the 1980 Patent and Trademark Law Amendment Act (P1 96–517), commonly known as the Bayh–Dole Act, represent gales of change as universities embrace new objectives that value active technology commercialization over older routines that promoted passive knowledge diffusion. However, the commercial realization of academic discoveries is ultimately dependent on the personal decisions and actions of the faculty, as faculty invention disclosures form the basis for university patents and subsequent licenses. Though the Bayh–Dole Act specifies that faculty members are to disclose their inventions to the university technology-transfer office, enforcement of this requirement has proven difficult. When individual faculty members choose to disclose their discoveries to the university's technology-transfer office, they signal that they are entrepreneurial in adopting the new initiative that aids in the transfer of knowledge out of the university to established companies or to use in the formation of new companies.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".