What does U-multirank tell us about knowledge transfer and research?

Autor:	Juan Antonio Dip
Rok vydání:	2021
Předmět:	Scheme (programming language) Information retrieval Computer science Importance Weight Principal (computer security) General Social Sciences Library and Information Sciences Computer Science Applications Weighting Data set Ranking Knowledge transfer computer computer.programming_language
Zdroj:	Scientometrics. 126:3011-3039
ISSN:	1588-2861 0138-9130
DOI:	10.1007/s11192-020-03838-2
Popis:	The economic and social need to spread knowledge between universities and industry has become increasingly evident in recent years. This paper presents a ranking based partly on research and knowledge transfer indicators from U-multirank data but using data-driven weights. The choice of specific weights and the comparison between ranks remain a sensitive topic. A restricted version of the benefit of the doubt method is implemented to build a new university ranking that includes an endogenous weighting scheme. Furthermore, a novel procedure is presented to compare the principal method with U-multirank. At the best of my knowledge, the U-multirank data set has been unapplied to achieve alternative rankings that include research and knowledge transfers dimensions. A significant result arises from the benefit of the doubt: the highest importance weight is assigned to the co-publications with industrial partners and interdisciplinary publication indicators. This paper fills a bit of the existing gap on the role of co-publications with industrial partners in the university efficiency around the world.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::aafe259f29fc4ee07ec09e65e266ae39 https://doi.org/10.1007/s11192-020-03838-2 Zobrazit plný text záznamu Full text from SpringerLink