Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 16. Dezember 2019)
1Tang, L. ; Hu, G. ; Liu, W.: Funding acknowledgment analysis : queries and caveats.
In: Journal of the Association for Information Science and Technology. 68(2017) no.3, S.790-794.
Abstract: Thomson Reuters's Web of Science (WoS) began systematically collecting acknowledgment information in August 2008. Since then, bibliometric analysis of funding acknowledgment (FA) has been growing and has aroused intense interest and attention from both academia and policy makers. Examining the distribution of FA by citation index database, by language, and by acknowledgment type, we noted coverage limitations and potential biases in each analysis. We argue that despite its great value, bibliometric analysis of FA should be used with caution.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23713/full.
2Tang, L. ; Hu, G.: Tracing the footprint of knowledge spillover : evidence from U.S.-China collaboration in nanotechnology.
In: Journal of the American Society for Information Science and Technology. 64(2013) no.9, S.1791-1801.
Abstract: The impact of international collaboration on research performance has been extensively explored over the past two decades. Most research, however, focuses on quantity and citation-based indicators. Using the turnover of keywords, this study develops an integrative approach, tracking and visualizing the shift of the research stream, and tests it within the context of U.S.-China collaboration in nanotechnology. The results show evidence in support of the linkage between the emergence of a new research stream of Chinese researchers when there is U.S.-China collaboration. We also find that the triggered research stream diffused further via extended coauthorship. Policy implications for science and technology development and resource allocation in the United States and China are discussed.
3Hu, G. ; Lin, H. ; Pan, W.: Conceptualizing and examining E-government service capability : a review and empirical study.
In: Journal of the American Society for Information Science and Technology. 64(2013) no.11, S.2379-2395.
Abstract: The effectiveness and efficiency of e-government (e-gov) services (EGS) are critical issues that have yet to be fully discussed. Inspired by successful practices in the areas of SERVQUAL, capability-based theories, and IT-related capability management, the efficient delivery of EGS should derive from the high capabilities of a government to provide such services. This article aims to develop a conceptual framework to assess and empirically examine EGSC using data from local governments in Mainland China. The fitness test and the case study prove that the conceptual framework was suitable in analyzing China's EGSC. In particular, the EGSC can be examined from 3 dimensions/layers: content service capability, service delivery capability, and on-demand capability. The results of the structural analysis illustrate the practical management applications of EGSC, which can facilitate the improvement of EGS.
4Hu, G. ; Zhou, S. ; Guan, J. ; Hu, X.: Towards effective document clustering : a constrained K-means based approach.
In: Information processing and management. 44(2008) no.4, S.1397-1409.
Abstract: Document clustering is an important tool for document collection organization and browsing. In real applications, some limited knowledge about cluster membership of a small number of documents is often available, such as some pairs of documents belonging to the same cluster. This kind of prior knowledge can be served as constraints for the clustering process. We integrate the constraints into the trace formulation of the sum of square Euclidean distance function of K-means. Then, the combined criterion function is transformed into trace maximization, which is further optimized by eigen-decomposition. Our experimental evaluation shows that the proposed semi-supervised clustering method can achieve better performance, compared to three existing methods.
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