References of "Scientometrics"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailAn Evolving International Research Collaboration Network: Spatial and Thematic Developments in Co-Authored Higher Education Research, 1998–2018
Fu, Yuan Chih; Marques, Marcelo; Tseng, Yuen-Hsien et al

in Scientometrics (2022), 127

Co-authored research articles in the disciplinarily heterogeneous field of higher education have dramatically increased in this century, largely driven, as in other fields, by rising international co ... [more ▼]

Co-authored research articles in the disciplinarily heterogeneous field of higher education have dramatically increased in this century, largely driven, as in other fields, by rising international co-authorships. We examine this evolving international collaboration network in higher education research over two decades. To do so, we apply automated bibliometric topic identification and social network analysis of 9,067 papers in 13 core higher education journals (1998–2018). Remarkable expansion in the volume of papers and co-authorships has, surprisingly, not resulted in a more diverse network. Rather, existing co-authorship patterns are strengthened, with the dominance of scholars from a few Anglophone countries largely maintained. Researchers globally seek to co-author with leading scholars in these countries, especially the US, UK, and Australia—at least when publishing in the leading general HE journals based there. Further, the two-mode social network analysis of countries and topics suggests that while Anglophone countries have led the development of higher education research, China and Germany, as leading research-producing countries, are increasingly influential within this world-spanning network. Topically, the vast majority of co-authored papers in higher education research focuses on individual-level phenomena, with organizational and system-level or country-level analysis constituting a (much) smaller proportion, despite policymakers’ emphasis on cross-national comparisons and the growing importance of university actorhood. We discuss implications thereof for the future of the multidisciplinary higher education field. [less ▲]

Detailed reference viewed: 192 (6 UL)
Full Text
Peer Reviewed
See detailAre Firms Withdrawing From Basic Research? An Analysis of Firm-level Publication Behaviour in Germany
Krieger, Bastian UL; Blind, Knut; Gruber, Sonia et al

in Scientometrics (2021), 126

Detailed reference viewed: 20 (3 UL)
Full Text
Peer Reviewed
See detailSemantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation
Kozlowski, Diego UL; Dusdal, Jennifer UL; Pang, Jun UL et al

in Scientometrics (2021)

Over the last century, we observe a steady and exponentially growth of scientific publications globally. The overwhelming amount of available literature makes a holistic analysis of the research within a ... [more ▼]

Over the last century, we observe a steady and exponentially growth of scientific publications globally. The overwhelming amount of available literature makes a holistic analysis of the research within a field and between fields based on manual inspection impossible. Automatic techniques to support the process of literature review are required to find the epistemic and social patterns that are embedded in scientific publications. In computer sciences, new tools have been developed to deal with large volumes of data. In particular, deep learning techniques open the possibility of automated end-to-end models to project observations to a new, low-dimensional space where the most relevant information of each observation is highlighted. Using deep learning to build new representations of scientific publications is a growing but still emerging field of research. The aim of this paper is to discuss the potential and limits of deep learning for gathering insights about scientific research articles. We focus on document-level embeddings based on the semantic and relational aspects of articles, using Natural Language Processing (NLP) and Graph Neural Networks (GNNs). We explore the different outcomes generated by those techniques. Our results show that using NLP we can encode a semantic space of articles, while with GNN we are able to build a relational space where the social practices of a research community are also encoded. [less ▲]

Detailed reference viewed: 103 (21 UL)
Full Text
Peer Reviewed
See detailProductivity and Mobility in Academic Research: Evidence from Mathematicians
Dubois, Pierre; Rochet, Jean-Charles; Schlenker, Jean-Marc UL

in Scientometrics (2014), 98(3), 1669-1701

Detailed reference viewed: 134 (3 UL)