Bibliographic record
Abstract
This paper aims to solve a puzzle in the technology commercialization literature with respect to timing: why some licensors’ observable quality measures that reduce technology information asymmetry are found to lead to early licensing, whereas others do not. The formal model in the paper shows that licensees’ positive beliefs in licensors’ capabilities and the price of licensing now will be substantially lower than licensing in the future promote early licensing. The number of competing licensees for the technology now reduces the licensing price difference whereas the number of licensees for the technology in the future increases the licensing price difference. The paper further argues that some of licensors’ observable qualities, e.g., patents in underlying technology, may both enhance licensees’ belief in licensors’ capabilities and erode the licensing price difference. As a result, we might not consistently observe early licensing when licensors have patents. Instead, if licensors own patents, we only observe early licensing when the future competition for the technology among licensees is sufficiently intense. By contrast, some of licensors’ observable qualities, e.g., alliance experiences, only affect licensees’ beliefs regarding licensors’ capabilities. Therefore, licensors’ prior alliance experiences always promote early licensing.
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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".