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China's communications industry is an important part of the electronic information industry, and plays a significant role in the national informatization process. In 2006, China issued its National Plans for Medium and Long-term Development of Science and Technology (2006-2020) (NPMLDST). Since 2006, there has been a rapid increase in the number of citations of China's international papers in the field of communications. In accordance with the goals listed in the NPMLDST, China needs to overtake several competitors by 2020 to be among the top five countries in the field of natural science field. By comparing two Essential Science Indicators (ESI) (i.e., the total number of citations and the number of citations per paper) for China and other countries, China's annual growth rate is found to exceed that of other influential countries in the field of science and technology, and exhibits evident growth-type characteristics. Besides, our study also shows that the shortage of high-quality academic papers in China is the main obstacle to improving the impact of China's academic publications.

This paper classifies common journal evaluation indicators into three categories, namely three first-level indicators. They are respectively the indicators on journal impact, on timeliness, and on journal characteristics. The data used here is drawn from the medical journals in CSTPCD, a citation database built by the Institute of Scientific and Technical Information of China. The three categories of indicators are correlated with one another, so a structural equation may be established. Then we calculate the value of three first-level indicators and give subjective weights to the indicators. The comprehensive evaluation upon the medical journals yields satisfactory results. By simulating the complex relationship among journal indicators, the structural equation can be used for the estimation of some implicit indicators and the screening of indicators. This approach provides a new perspective for scientific and technological evaluation in general sense. It should be noted that the availability of basic data and the rationality of modeling bear much upon the evaluation results.

Journal citation measures are one of the most widely used bibliometric tools. The most well-known measure is the ISI Impact Factor, under the standard definition, the impact factor of journal j in a given year is the average number of citations received by papers published in the previous two years of journal j. However, the impact factor has its “intrinsic” limitations, it is a ranking measure based fundamentally on a pure counting of the in-degrees of nodes in the network, and its calculation does not take into account the “impact” or “prestige” of the journals in which the citations appear. Google’s PageRank algorithm and Kleinberg’s HITS method are webpage ranking algorithm, they compute the scores of webpages based on a combination of the number of hyperlinks that point to the page and the status of pages that the hyperlinks originate from, a page is important if it is pointed to by other important pages. We demonstrate how popular webpage algorithm PageRank and HITS can be used ranking journal, and we compared ISI impact factor, PageRank and HITS for journal ranking, and with PageRank and HITS compute respectively including self-citation and non self-citation, and discussed the merit and shortcomings and the scope of application that the various algorithms are used to rank journal.

This paper analyzes the relation between peer review and multi-indicators evaluation for scientific and technical assessment. Based on the data of the 2007 Times Higher-QS world university rankings, we use two methods, multiple linear regression analysis and kappa agreement test, for analysis. The results show that the data abundance affects the agreement between peer review and multi-indicators evaluation. We conclude that comprehensive evaluation in combination with peer review is a better choice in lack of data. Regression goodness-of-fit can be used to assess data abundance. Peer review should be the basis for the choice of indicators and indicator weights. However, multi-indicators evaluation is more stable and objective.