Search (2 results, page 1 of 1)

  • × author_ss:"Leydesdorff, L."
  • × author_ss:"Zhou, P."
  • × year_i:[2000 TO 2010}
  1. Zhou, P.; Leydesdorff, L.: ¬A comparison between the China Scientific and Technical Papers and Citations Database and the Science Citation Index in terms of journal hierarchies and interjournal citation relations (2007) 0.00 
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    Abstract
    The journal structure in the China Scientific and Technical Papers and Citations Database (CSTPCD) is analyzed from three perspectives: the database level, the specialty level, and the institutional level (i.e., university journals vs. journals issued by the Chinese Academy of Sciences). The results are compared with those for (Chinese) journals included in the Science Citation Index (SCI). The frequency of journal-journal citation relations in the CSTPCD is an order of magnitude lower than in the SCI. Chinese journals, especially high-quality journals, prefer to cite international journals rather than domestic ones; however, Chinese journals do not get an equivalent reception from their international counterparts. The international visibility of Chinese journals is low, but varies among fields of science. Journals of the Chinese Academy of Sciences have a better reception in the international scientific community than university journals.
    Type
    a
  2. Leydesdorff, L.; Zhou, P.: Co-word analysis using the Chinese character set (2008) 0.00 
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    Abstract
    Until recently, Chinese texts could not be studied using co-word analysis because the words are not separated by spaces in Chinese (and Japanese). A word can be composed of one or more characters. The online availability of programs that separate Chinese texts makes it possible to analyze them using semantic maps. Chinese characters contain not only information but also meaning. This may enhance the readability of semantic maps. In this study, we analyze 58 words which occur 10 or more times in the 1,652 journal titles of the China Scientific and Technical Papers and Citations Database. The word-occurrence matrix is visualized and factor-analyzed.
    Type
    a