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Abstract  

Most studies of scholarly influence within disciplines using citation data do not investigate the extent of an individual’s influence; does it extend over a number of years with a sequence of publications or is it confined to a short period and a small number of publications? Using bibliographic data from a series of quadrennial reports into developments in UK geography, this paper finds that few authors are cited on more than one occasion.

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. Case , D. O. Higgins , G. M. 2000 How can we investigate citation behavior?:A study of reasons for literature in communication Journal of The American Society for Information

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hand, the number of publications dedicated to electronic government issues in developing countries has increased in recent years. Researchers have analyzed the growing amount of electronic government literature while pursuing different objectives. Many

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Abstract  

Using statistical method, the author analyzed the citation rate of articles published in Chinese Science Bulletin (CSB) between 1995 and 1999 in Science Citation Index Expanded (SCIE) databases. Results indicated that: 1. Majority of authors who published in CSB were Chinese; 2. The articles were basically cited by the authors themselves in the first year after publication; 3. The peak of total citation rate appeared in the third year after publication and the peak of non-self-citation rate was further delayed. There are relatively high self-citation rates of articles from CSB and most of these citations are from Chinese scientific journals. This indicates that our citation environment is limited to a closed circle. The author, therefore, proposed a strategy for changing the current conditions of Chinese scientific journals to raise their influence.

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Abstract  

The most popular method for judging the impact of biomedical articles is citation count which is the number of citations received. The most significant limitation of citation count is that it cannot evaluate articles at the time of publication since citations accumulate over time. This work presents computer models that accurately predict citation counts of biomedical publications within a deep horizon of 10 years using only predictive information available at publication time. Our experiments show that it is indeed feasible to accurately predict future citation counts with a mixture of content-based and bibliometric features using machine learning methods. The models pave the way for practical prediction of the long-term impact of publication, and their statistical analysis provides greater insight into citation behavior.

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Abstract  

The present paper describes the application of growth models as suggested by Egghe and Ravichadra Rao (Scientometrics 25:5–46, 1992). The scope of the paper is limited to study the growth and dynamics of Indian and Chinese publications in the field of liquid crystals research (1997–2006).

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universities (Shin, in press). However, the previous literature published in international journals does not adequately address the social forces shaping U-I-G relations development and innovation diffusion in Asia. In order to investigate the TH or U

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media technologies (Baez et al. 2010 ) may reduce the onus of using the scientific literature. Indeed, in other knowledge-intensive work such as software development, enhanced search capability leads to greater component reuse (Banker et al. 1993

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conference literature), the subsequent critical discoveries (evident via original scientific, conference literature and patents), and the transitioning through the various TRLs ultimately to commercial application. Methodology

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