Search (55 results, page 1 of 3)

  • × theme_ss:"Informetrie"
  • × year_i:[2000 TO 2010}
  1. Burrell, Q.L.: Predicting future citation behavior (2003) 0.06
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    Abstract
    In this article we further develop the theory for a stochastic model for the citation process in the presence of obsolescence to predict the future citation pattern of individual papers in a collection. More precisely, we investigate the conditional distribution-and its mean- of the number of citations to a paper after time t, given the number of citations it has received up to time t. In an important parametric case it is shown that the expected number of future citations is a linear function of the current number, this being interpretable as an example of a success-breeds-success phenomenon.
    Date
    29. 3.2003 19:22:48
  2. Thelwall, M.; Ruschenburg, T.: Grundlagen und Forschungsfelder der Webometrie (2006) 0.06
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    Date
    4.12.2006 12:12:22
  3. Althouse, B.M.; West, J.D.; Bergstrom, C.T.; Bergstrom, T.: Differences in impact factor across fields and over time (2009) 0.04
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    Date
    23. 2.2009 18:22:28
  4. Egghe, L.: Untangling Herdan's law and Heaps' law : mathematical and informetric arguments (2007) 0.03
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    Abstract
    Herdan's law in linguistics and Heaps' law in information retrieval are different formulations of the same phenomenon. Stated briefly and in linguistic terms they state that vocabularies' sizes are concave increasing power laws of texts' sizes. This study investigates these laws from a purely mathematical and informetric point of view. A general informetric argument shows that the problem of proving these laws is, in fact, ill-posed. Using the more general terminology of sources and items, the author shows by presenting exact formulas from Lotkaian informetrics that the total number T of sources is not only a function of the total number A of items, but is also a function of several parameters (e.g., the parameters occurring in Lotka's law). Consequently, it is shown that a fixed T(or A) value can lead to different possible A (respectively, T) values. Limiting the T(A)-variability to increasing samples (e.g., in a text as done in linguistics) the author then shows, in a purely mathematical way, that for large sample sizes T~ A**phi, where phi is a constant, phi < 1 but close to 1, hence roughly, Heaps' or Herdan's law can be proved without using any linguistic or informetric argument. The author also shows that for smaller samples, a is not a constant but essentially decreases as confirmed by practical examples. Finally, an exact informetric argument on random sampling in the items shows that, in most cases, T= T(A) is a concavely increasing function, in accordance with practical examples.
  5. Nicolaisen, J.: Citation analysis (2007) 0.02
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    Date
    13. 7.2008 19:53:22
  6. 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
  7. Burrell, Q.L.: "Type/Token-Taken" informetrics : Some comments and further examples (2003) 0.02
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    Abstract
    Egghe has propounded the notion of Type/Token-Taken (T/TT) informetrics. In this note we show how his ideas relate to ones that are already well known in informetrics and resolve some of the specific problems posed.
  8. Egghe, L.; Ravichandra Rao, I.K.: ¬The influence of the broadness of a query of a topic on its h-index : models and examples of the h-index of n-grams (2008) 0.02
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    Abstract
    The article studies the influence of the query formulation of a topic on its h-index. In order to generate pure random sets of documents, we used N-grams (N variable) to measure this influence: strings of zeros, truncated at the end. The used databases are WoS and Scopus. The formula h=T**1/alpha, proved in Egghe and Rousseau (2006) where T is the number of retrieved documents and is Lotka's exponent, is confirmed being a concavely increasing function of T. We also give a formula for the relation between h and N the length of the N-gram: h=D10**(-N/alpha) where D is a constant, a convexly decreasing function, which is found in our experiments. Nonlinear regression on h=T**1/alpha gives an estimation of , which can then be used to estimate the h-index of the entire database (Web of Science [WoS] and Scopus): h=S**1/alpha, , where S is the total number of documents in the database.
  9. 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
  10. 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.
  11. Burrell, Q.L.: Will this paper ever be cited? (2002) 0.02
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    Abstract
    A recently proposed stochastic model to describe the citation process in the presence of obsolescence is used to answer the question: If a paper has not been cited by time t after its publication, what is the probability that it will ever be cited?
  12. Marchant, T.: Score-based bibliometric rankings of authors (2009) 0.02
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  13. Tscherteu, G.; Langreiter, C.: Explorative Netzwerkanalyse im Living Web (2009) 0.02
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    Source
    Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini
  14. Bookstein, A.; Raita, T.: Discovering term occurence structure in text (2001) 0.01
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  15. Liang, L.: R-Sequences : relative indicators for the rhythm of science (2005) 0.01
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    Abstract
    Like most activities in the world, scientific evolution has its own rhythm. How can this evolutionary rhythm be described and made visible? Do different fields have different rhythms, and how can they be measured? In order to answer these questions a relative indicator, called R-sequence, was designed. This indicator is time dependent, derived from publication and citation data, but independent of the absolute number of publications, as weIl as the absolute number of citations, and can therefore be used in a comparison of different scientific fields, nations, Institutes, or journals. Two caiculation methods of the R-sequence-the triangle method and the parallelogram method-are introduced. As a case study JASIS(T)'s R-sequence has been obtained.
  16. Brody, T.; Harnad, S.; Carr, L.: Earlier Web usage statistics as predictors of later citation impact (2006) 0.01
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  17. Lafouge, T.; Prime-Claverie, C.: Production and use of information : Characterization of informetric distributions using effort function and density function. Exponential informetric process (2005) 0.01
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  18. Brooks, T.: Citer motivations (2009) 0.01
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  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

Languages

  • e 49
  • d 6

Types

  • a 55
  • el 1
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