-
Nicolaisen, J.: Citation analysis (2007)
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- Date
- 13. 7.2008 19:53:22
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Nicolaisen, J.: ¬The J-shaped distribution of citedness (2002)
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- Abstract
- A new approach for investigating the correlation between research quality and citation counts is presented and applied to a case study of the relationship between peer evaluations reflected in scholarly book reviews and the citation frequencies of reviewed books. Results of the study designate a J-shaped distribution between the considered variables, presumably caused by a skewed allocation of negative citations. The paper concludes with suggestions for further research.
-
Mingers, J.; Burrell, Q.L.: Modeling citation behavior in Management Science journals (2006)
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- Date
- 26.12.2007 19:22:05
-
Chen, C.-M.: Classification of scientific networks using aggregated journal-journal citation relations in the Journal Citation Reports (2008)
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- Abstract
- I propose an approach to classifying scientific networks in terms of aggregated journal-journal citation relations of the ISI Journal Citation Reports using the affinity propagation method. This algorithm is applied to obtain the classification of SCI and SSCI journals by minimizing intracategory journal-journal (J-J) distances in the database, where distance between journals is calculated from the similarity of their annual citation patterns with a cutoff parameter, t, to restrain the maximal J-J distance. As demonstrated in the classification of SCI journals, classification of scientific networks with different resolution is possible by choosing proper values of t. Twenty journal categories in SCI are found to be stable despite a difference of an order of magnitude in t. In our classifications, the level of specificity of a category can be found by looking at its value of RJ (the average distance of members of a category to its representative journal), and relatedness of category members is implied by the value of DJ-J (the average DJ-J distance within a category). Our results are consistent with the ISI classification scheme, and the level of relatedness for most categories in our classification is higher than their counterpart in the ISI classification scheme.
-
Egghe, L.: Empirical and combinatorial study of country occurrences in multi-authored papers (2006)
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- Abstract
- Papers written by several authors can be classified according to the countries of the author affiliations. The empirical part of this paper consists of two datasets. One dataset consists of 1,035 papers retrieved via the search "pedagog*" in the years 2004 and 2005 (up to October) in Academic Search Elite which is a case where phi(m) = the number of papers with m =1, 2,3 ... authors is decreasing, hence most of the papers have a low number of authors. Here we find that #, m = the number of times a country occurs j times in a m-authored paper, j =1, ..., m-1 is decreasing and that # m, m is much higher than all the other #j, m values. The other dataset consists of 3,271 papers retrieved via the search "enzyme" in the year 2005 (up to October) in the same database which is a case of a non-decreasing phi(m): most papers have 3 or 4 authors and we even find many papers with a much higher number of authors. In this case we show again that # m, m is much higher than the other #j, m values but that #j, m is not decreasing anymore in j =1, ..., m-1, although #1, m is (apart from # m, m) the largest number amongst the #j,m. The combinatorial part gives a proof of the fact that #j,m decreases for j = 1, m-1, supposing that all cases are equally possible. This shows that the first dataset is more conform with this model than the second dataset. Explanations for these findings are given. From the data we also find the (we think: new) distribution of number of papers with n =1, 2,3,... countries (i.e. where there are n different countries involved amongst the m (a n) authors of a paper): a fast decreasing function e.g. as a power law with a very large Lotka exponent.
-
Egghe, L.: Relations between the continuous and the discrete Lotka power function (2005)
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- Abstract
- The discrete Lotka power function describes the number of sources (e.g., authors) with n = 1, 2, 3, ... items (e.g., publications). As in econometrics, informetrics theory requires functions of a continuous variable j, replacing the discrete variable n. Now j represents item densities instead of number of items. The continuous Lotka power function describes the density of sources with item density j. The discrete Lotka function one obtains from data, obtained empirically; the continuous Lotka function is the one needed when one wants to apply Lotkaian informetrics, i.e., to determine properties that can be derived from the (continuous) model. It is, hence, important to know the relations between the two models. We show that the exponents of the discrete Lotka function (if not too high, i.e., within limits encountered in practice) and of the continuous Lotka function are approximately the same. This is important to know in applying theoretical results (from the continuous model), derived from practical data.
-
Janes, J.: Categorical relationships : chi-square (2001)
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-
Pudovkin, A.I.; Garfield, E.: Algorithmic procedure for finding semantically related journals (2002)
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- Abstract
- Journal Citation Reports provides a classification of journals most heavily cited by a given journal and which most heavily cite that journal, but size variation is not taken into account. Pudovkin and Garfield suggest a procedure for meeting this difficulty. The relatedness of journal i to journal j is determined by the number of citations from journal i to journal j in a given year normalized by the product of the papers published in the j journal in that year times the number of references cited in the i journal in that year. A multiplier of ten to the sixth is suggested to bring the values into an easily perceptible range. While citations received depend upon the overall cumulative number of papers published by a journal, the current year is utilized since that data is available in JCR. Citations to current year papers would be quite low in most fields and thus not included. To produce the final index, the maximum of the A citing B value, and the B citing A value is chosen and used to indicate the closeness of the journals. The procedure is illustrated for the journal Genetics.
-
Wettlauf der Wissenschaft (2004)
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- Footnote
- Vgl.: http://www.i-med.ac.at/mypoint/news/2004051201.xml (cf) (Quelle: U.S. Is Losing Its Dominance In the Sciences, by WI LLIAM J. BROAD, New York Times, May 3, 2004)
- Source
- Online Mitteilungen. 2004, Nr.79, S.22-23 [=Mitteilungen VÖB 57(2004) H.2]
-
Cheng, S.; YunTao, P.; JunPeng, Y.; Hong, G.; ZhengLu, Y.; ZhiYu, H.: PageRank, HITS and impact factor for journal ranking (2009)
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- Abstract
- 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.
-
Chen, C.; Kuljis, J.: ¬The rising landscape : a visual exploration of superstring revolutions in physics (2003)
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-
Bar-Ilan, J.; Peritz, B.C.: Evolution, continuity, and disappearance of documents on a specific topic an the Web : a longitudinal study of "informetrics" (2004)
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-
Frandsen, T.F.; Nicolaisen, J.: Intradisciplinary differences in database coverage and the consequences for bibliometric research (2008)
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