Bibliographic record
Abstract
Successful technology transfer of innovations arising from university research is often hindered by the lack of development funds to add value to these nascent discoveries. Within a university context, ‘gap funding’ is, for example, grant research funding that supports the demonstration of technical feasibility, prototype development, and/or assists with broadening patent claims and strengthening licensing opportunities. It is this early development stage that constitutes the bottleneck in which the transfer of promising technologies in academia can often languish or come to a halt from the lack of even a modest amount of such funding. This paper reports on measured outcomes of two such gap funding programmes at the authors' institution, presented as case studies that demonstrate the importance of this type of funding, and provides several recommendations for grants administration. In addition, results of a survey conducted on the status of gap funding programmes at other academic institutions in North America are presented. Surprisingly few such programmes exist in North America and very few have reported outcomes. The case study results support the conclusion that gap funding programmes are critical to technology development and transfer within a university setting and can provide valuable returns on the investment. These returns include enhancing patenting and licensing efforts as well as various collateral benefits such as the number of publications created; students trained; spin-offs formed; and the leveraging induced as measured by the amount of follow-on federal and industrial sponsored research dollars.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".