Search (84 results, page 1 of 5)

  • × language_ss:"e"
  • × theme_ss:"Citation indexing"
  • × theme_ss:"Informetrie"
  • × year_i:[2000 TO 2010}
  1. Nicolaisen, J.: Citation analysis (2007) 0.06 
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    Date
    13. 7.2008 19:53:22
    Language
    e
  2. Van der Veer Martens, B.: Do citation systems represent theories of truth? (2001) 0.05 
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    Date
    22. 7.2006 15:22:28
    Language
    e
  3. Larivière, V.; Gingras, Y.; Archambault, E.: ¬The decline in the concentration of citations, 1900-2007 (2009) 0.03 
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    Date
    22. 3.2009 19:22:35
    Language
    e
  4. Chan, H.C.; Kim, H.-W.; Tan, W.C.: Information systems citation patterns from International Conference on Information Systems articles (2006) 0.02 
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    Date
    3. 1.2007 17:22:03
    Language
    e
  5. Mingers, J.; Burrell, Q.L.: Modeling citation behavior in Management Science journals (2006) 0.02 
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    Date
    26.12.2007 19:22:05
    Language
    e
  6. Ahlgren, P.; Jarneving, B.; Rousseau, R.: Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient (2003) 0.02 
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    Abstract
    Ahlgren, Jarneving, and. Rousseau review accepted procedures for author co-citation analysis first pointing out that since in the raw data matrix the row and column values are identical i,e, the co-citation count of two authors, there is no clear choice for diagonal values. They suggest the number of times an author has been co-cited with himself excluding self citation rather than the common treatment as zeros or as missing values. When the matrix is converted to a similarity matrix the normal procedure is to create a matrix of Pearson's r coefficients between data vectors. Ranking by r and by co-citation frequency and by intuition can easily yield three different orders. It would seem necessary that the adding of zeros to the matrix will not affect the value or the relative order of similarity measures but it is shown that this is not the case with Pearson's r. Using 913 bibliographic descriptions form the Web of Science of articles form JASIS and Scientometrics, authors names were extracted, edited and 12 information retrieval authors and 12 bibliometric authors each from the top 100 most cited were selected. Co-citation and r value (diagonal elements treated as missing) matrices were constructed, and then reconstructed in expanded form. Adding zeros can both change the r value and the ordering of the authors based upon that value. A chi-squared distance measure would not violate these requirements, nor would the cosine coefficient. It is also argued that co-citation data is ordinal data since there is no assurance of an absolute zero number of co-citations, and thus Pearson is not appropriate. The number of ties in co-citation data make the use of the Spearman rank order coefficient problematic.
    Date
    9. 7.2006 10:22:35
    Language
    e
  7. ¬The Web of knowledge : Festschrift in honor of Eugene Garfield (2000) 0.00 
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    Biographed
    Garfield, E.
    Footnote
    Rez. in: Knowledge organization 28(2001) no.1, S.45-46 (M.J. López Huertas u. E. Jiménez-Contreras); Password 2002, H.3, S.14-19 (W.G. Stock)
    Language
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  8. Bensman, S.J.: Garfield and the impact factors (2007) 0.00 
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  9. He, Y.; Hui, S.C.: Mining a web database for author cocitation analysis (2002) 0.00 
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  10. Cronin, B.: Bibliometrics and beyond : some thoughts on web-based citation analysis (2001) 0.00 
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  11. Kurtz, M.J.; Eichhorn, G.; Accomazzi, A.; Grant, C.; Demleitner, M.; Henneken, E.; Murray, S.S.: ¬The effect of use and access on citations (2005) 0.00 
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    Abstract
    It has been shown (Lawrence, S. (2001). Online or invisible? Nature, 411, 521) that journal articles which have been posted without charge on the internet are more heavily cited than those which have not been. Using data from the NASA Astrophysics Data System (ads.harvard.edu) and from the ArXiv e-print archive at Cornell University (arXiv.org) we examine the causes of this effect.
    Language
    e
  12. Larsen, B.: Exploiting citation overlaps for information retrieval : generating a boomerang effect from the network of scientific papers (2002) 0.00 
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  13. Garfield, E.; Pudovkin, A.I.; Istomin, V.S.: Why do we need algorithmic historiography? (2003) 0.00 
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  14. Cronin, B.: Semiotics and evaluative bibliometrics (2000) 0.00 
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  15. Száva-Kováts, E.: Indirect-collective referencing (ICR) in the elite journal literature of physics : I: a literature science study on the journal level (2001) 0.00 
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  16. Brody, T.; Harnad, S.; Carr, L.: Earlier Web usage statistics as predictors of later citation impact (2006) 0.00 
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    Abstract
    The use of citation counts to assess the impact of research articles is well established. However, the citation impact of an article can only be measured several years after it has been published. As research articles are increasingly accessed through the Web, the number of times an article is downloaded can be instantly recorded and counted. One would expect the number of times an article is read to be related both to the number of times it is cited and to how old the article is. The authors analyze how short-term Web usage impact predicts medium-term citation impact. The physics e-print archive-arXiv.org-is used to test this.
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  17. Abt, H.A.; Garfield, E.: Is the relationship between numbers of references and paper lengths the same for all sciences? (2002) 0.00 
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  18. Burrell, Q.L.: Will this paper ever be cited? (2002) 0.00 
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  19. Cronin, B.; Shaw, D.: Banking (on) different forms of symbolic capital (2002) 0.00 
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  20. Nicolaisen, J.: ¬The J-shaped distribution of citedness (2002) 0.00 
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