Bibliographic record
Abstract
Higher education and private industry have struggled to overcome divergent research priorities and objectives, but shrinking R&D budgets and a need for innovation have sparked new overtures for collaborations. An award-winning Virginia initiative has taken up the challenge of improving communication between higher education researchers and business representatives by building trust and identifying needs for both sectors. Europe is ahead in encouraging cooperation between the private sector and universities. Markus Perkmann, research fellow at the Imperial College Business School in London, and Ammon Salter, research director of the UK Innovation Research Centre and EU Commissioner for Research, Innovation and Science, wrote about the trend in a recent issue of Canada's Financial Post. Rather than merely licensing inventions, another often under-appreciated opportunity for firms is to get help from universities during the whole life cycle of their innovation projects, Perkmann and Salter wrote. For example, in the United Kingdom, businesses already spend more than 20 times more on university collaboration than on licensing technology from universities ... businesses that don't work with universities may be missing opportunities of significant proportions. In Virginia where higher education is feeling the pinch of reduced federal research dollars and the work force remains hungry for new industry jobs, collaborations are developing at the very starting point of innovation: where both sectors learn what each other has to offer. Linking Innovation Industry and Commercialization (LIINC) was started at Virginia University in 2012 to accelerate commercialization of university-originated research and jumpstart collaborative projects by bringing researchers and business people together at informal events designed to nurture relationships. Most events include brief presentations, poster sessions, and light refreshments, but in every case, the evening concludes with one-on-one discussions that often lead to productive activity. Separate events have been held for bioscience and biomedical technologies, security and intelligence, energy and the environment, and life sciences and natural resources research. had its roots in a 2011 report, West Virginia Blueprint for Technology-Based Economic Development, prepared by the Battelle Institute's Technology Partnership Practice and an economic development organization called TechConnectWV. In addition to identifying a need to act boldly and quickly to grow research, technology transfer, and commercialization, the report noted that Virginia trails the national average in securing industry support for university-based R&D. According to the report, From 2002 to 2009, an average of 3.3% of total R&D expenditures at Virginia colleges and universities came from industry compared with 5.4% in the United States. Blueprint suggested that universities should aim to match the national average by 2020. That recommendation sparked the program. According to Lindsay Emery, the business development manager in charge of LIINC, had a record of research performance, a technology transfer office and a business incubator but no mechanism to actively connect faculty and students to the private sector. We needed to bring the University's talent base to the attention of industry partners. LIINC, which attracted funding from the Pittsburgh-based Claude Worthington Benedum Foundation and strong support from WVU STEM deans, was designed to provide that connection. Benedum Foundation provided $132,000 to support LIINC. The Benedum Foundation has been supporting the growth of technology-based economic development in both Virginia and Southwest Pennsylvania for several years, explained Mary Hunt, senior program manager at the Foundation. LIINC is a program that enables the many players in the technology-based economy to come together and explore new opportunities. …
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.151 | 0.058 |
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".