Bibliographic record
Abstract
This annual survey by The Association of University Technology Managers is a summary of technology licensing and related performance information for United States and Canadian academic and non profit institutions, and patent management and investment firms. The management of intellectual property in order to make academic research results available to the public in the form of commercial products is explored. Quantitative information from AUTM members using the AUTM Licensing Survey instrument is presented. Information is gleaned about new programs as well asmore established programs. Because the technology transfer process takes anumber of years, the data from programs at different points in the process arenot comparable. The results indicatethe long-term nature of technology transfer and short-term impacts of economic conditions. The survey also reflects the difficulty that university licensing professionals encountered in the 2002 fiscal year due to decreases in funding linked to licenses and options. Furthermore, though the venture capital industry experienced a significant decline in start-ups and those going out of business increased sharply, universities continue to reap the rewards of transactions and partnerships since the passage of the Bayh-Dole Act. Finally, total royalty income isanalyzed on various levels. (JSD)
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.015 |
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".