Engaging the inventor: exploring licensing strategies for university inventions and the role of latent knowledge
Bibliographic record
Abstract
Abstract A significant portion of knowledge generated by university inventors remains latent (uncodified but codifiable), even though this information is valuable to firms that have licensed their inventions and famously strong incentives exist to disseminate academic findings widely. However, the licensee may access and exploit this latent knowledge by engaging the inventor during the development phase. This paper examines the hypothesis that licensing strategies that directly engage the inventor increase the likelihood and degree of commercialization success. While this may seem somewhat apparent, firms in the sample under investigation vary substantially in the degree to which they engage the inventor: one third of the sample does not engage the inventor at all. On the other hand, the hypothesis might seem surprising given the norms of open science under which university labs are expected to operate. Regression analyses based on a unique dataset of 124 license agreements associated with inventions from MIT support the hypothesis and generate results that are robust to a variety of controls. Copyright © 2005 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.017 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".