Bibliographic record
Abstract
This annual survey by The Association ofUniversity Technology Managers is a summary of technology licensing and related performance information for United States and Canadian academic and non profit institutions, and a few patent management and investment firms, for the fiscalyear 2003. Quantitative information from AUTM members using the AUTM Licensing Survey instrument is presented. In a change from previous reports, survey results arepresented separately for the 198 United States and 37 Canadian universities,hospitals, institutes and technology investment firms responding. Presenting two country reports highlights the distinct conditions, challenges, and opportunities of the two countries. In each country report,results are summarized following the order of the technology transfer process: resources devoted to technology transfer, research support, invention disclosures, patent applications, issued patents, licensing information, and startup companies. Expanded in this year's report is discussion of the ultimate goal ofacademic and nonprofit technology transfer; included are summaries of 25 technology transfer success stories in the United States (16) and Canada (9),most of which are health-care related. The results indicate the long-termnature of technology transfer and short-term impacts of economic conditions. Academic technology transfer continues adapting to difficult early-stagefinancial market conditions occurring since the stock market collapse of early2000. Rate of spinout creation declined and companies went out of business at anhistorically high rate. But overall technology transfer continues toadvance. The highlighting of Canada shows how differently technology transfer is practiced there; Canadian performance measure for 2003 out pacedthose in the United States. Appendices include survey methodology, definitions, the survey form, and survey totals for U.S. and Canadian institutions. (JSD/TNM)
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 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) | 0.019 | 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".