Notice bibliographique
Résumé
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. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,011 | 0,003 |
| Communication savante | 0,016 | 0,006 |
| Science ouverte | 0,002 | 0,016 |
| Intégrité de la recherche | 0,005 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,151 | 0,058 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».