Search Results
Abstract
The qualitative label ‘international journal’ is used widely, including in national research quality assessments. We determined the practicability of analysing internationality quantitatively using 39 conservation biology journals, providing a single numeric index (IIJ) based on 10 variables covering the countries represented in the journals’ editorial boards, authors and authors citing the journals’ papers. A numerical taxonomic analysis refined the interpretation, revealing six categories of journals reflecting distinct international emphases not apparent from simple inspection of the IIJs alone. Categories correlated significantly with journals’ citation impact (measured by the Hirsch index), with their rankings under the Australian Commonwealth’s ‘Excellence in Research for Australia’ and with some countries of publication, but not with listing by ISI Web of Science. The assessments do not reflect on quality, but may aid editors planning distinctive journal profiles, or authors seeking appropriate outlets.
Abstract
This paper suggests an empirical framework to classify research collaboration activities with developed indicators that carry on a previous theoretical framework (Wagner [Science and Technology Policy for Development, 2006]; Wagner et al. [Linking effectively: Learning lessons from successful collaboration in science and technology. DB-345-OSTP, 2002]) by employing the Gaussian mixture model, an advanced probabilistic clustering analysis. By further exploring the method upon a profound evidence-based reflection of actual phenomena, this paper also proposes an exploratory analysis to manage and evaluate research projects upon their differentiated classification in a preceding perspective of research collaboration and R&D management. In addition, the results show that international collaboration tends to be associated with more evenly committed collaboration, and that collaboration featuring a higher degree of funding or dispersed commitments generally results in larger outcomes than research clustered on the opposite side of the framework.
Abstract
The aim of this paper is to present new ideas in evaluating Shanghai University's Academic Ranking of World Universities (ARWU). In particular, this paper shall try to determine whether the normalization of data affects University ranks. In accordance with this, both the normalized and original (raw) data for each of the six variables has been obtained. Based on a sample containing the 54 US universities which are placed in the ARWU top 100, the statistical I-distance method was performed. The results showed great inconsistencies between university ranks obtained for the original and normalized data. These findings were then analyzed and the universities that had the greatest fluctuation in their ranks were noted.
