Search (262 results, page 2 of 14)

  • × theme_ss:"Informetrie"
  1. Torres-Salinas, D.; Gorraiz, J.; Robinson-Garcia, N.: ¬The insoluble problems of books : what does Altmetric.com have to offer? (2018) 0.02 
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    Date
    20. 1.2015 18:30:22
  2. Su, Y.; Han, L.-F.: ¬A new literature growth model : variable exponential growth law of literature (1998) 0.02 
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    Date
    22. 5.1999 19:22:35
  3. Van der Veer Martens, B.: Do citation systems represent theories of truth? (2001) 0.02 
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    Date
    22. 7.2006 15:22:28
  4. Diodato, V.: Dictionary of bibliometrics (1994) 0.02 
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    Footnote
    Rez. in: Journal of library and information science 22(1996) no.2, S.116-117 (L.C. Smith)
  5. Bookstein, A.: Informetric distributions : I. Unified overview (1990) 0.02 
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    Date
    22. 7.2006 18:55:29
  6. Bookstein, A.: Informetric distributions : II. Resilience to ambiguity (1990) 0.02 
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    Date
    22. 7.2006 18:55:55
  7. King, J.: ¬A review of bibliometric and other indicators and their role in research evaluation (1987) 0.02 
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  8. Williams, J.; Clark, J.D.: ¬The information explosion : fact or myth? (1992) 0.02 
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  9. Kurtz, M.; Bollen, J.: Usage bibliometrics (2010) 0.02 
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  10. Lewison, G.: ¬The work of the Bibliometrics Research Group (City University) and associates (2005) 0.02 
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    Date
    20. 1.2007 17:02:22
  11. Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014) 0.02 
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    Date
    18. 3.2014 19:13:22
  12. Lancaster, F.W.; Li, J.: ¬The law of constant accessibility of information (1988/89) 0.02 
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  13. Nicolaisen, J.: ¬The J-shaped distribution of citedness (2002) 0.02 
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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.
  14. Chen, C.-M.: Classification of scientific networks using aggregated journal-journal citation relations in the Journal Citation Reports (2008) 0.02 
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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.
  15. Wang, C.: Bibliometrics : a textbook (1990) 0.02 
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    Footnote
    Rez. in: Journal of information, communication, and library science. 2(1995) no.2, S.84-85 (J. Qin)
  16. Bornmann, L.; Bauer, J.; Haunschild, R.: Distribution of women and men among highly cited scientists (2015) 0.02 
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  17. Egghe, L.: Empirical and combinatorial study of country occurrences in multi-authored papers (2006) 0.02 
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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.
  18. Egghe, L.: Relations between the continuous and the discrete Lotka power function (2005) 0.01 
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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.
  19. Raan, A.F.J. van: Statistical properties of bibliometric indicators : research group indicator distributions and correlations (2006) 0.01 
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    Date
    22. 7.2006 16:20:22
  20. Larivière, V.; Gingras, Y.; Archambault, E.: ¬The decline in the concentration of citations, 1900-2007 (2009) 0.01 
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    Date
    22. 3.2009 19:22:35

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