Search (23 results, page 1 of 2)

  • × theme_ss:"Citation indexing"
  • × theme_ss:"Informetrie"
  • × type_ss:"a"
  1. Nicolaisen, J.: Citation analysis (2007) 0.00
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    Date
    13. 7.2008 19:53:22
  2. Van der Veer Martens, B.: Do citation systems represent theories of truth? (2001) 0.00
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    Date
    22. 7.2006 15:22:28
  3. Larivière, V.; Gingras, Y.; Archambault, E.: ¬The decline in the concentration of citations, 1900-2007 (2009) 0.00
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    Date
    22. 3.2009 19:22:35
  4. Garfield, E.; Pudovkin, A.I.; Istomin, V.S.: Why do we need algorithmic historiography? (2003) 0.00
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    Date
    29. 3.2003 19:52:23
  5. 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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    Date
    27.12.2007 17:16:29
  6. Morris, S.A.; Yen, G.; Wu, Z.; Asnake, B.: Time line visualization of research fronts (2003) 0.00
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    Date
    29. 3.2003 19:51:59
  7. Spasser, M.A.: ¬The enacted fate of undiscovered public knowledge (1997) 0.00
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    Abstract
    In a series of articles, Don Swanson explores the problem of associating two or more literatures that are logically, or substantively, related, but bibliographically noninteractive. He has called these implicit links among published literatures undicovered public knowledge. This article explores the fate of Swanson's ideas, using citation content analysis both to determine which authors have utilized Swanson's ideas and to examine the uses to which they have been put. The results suggest that while Swanson has received significant attention from the library and information science community, his ideas have not been widely cited in biomedical disciplines, and, when cited, only with rhetorically dismissive qualifications that detracts from their facticity. These results are interpreted as a failed instance of interdisciplinarity communication, and several explanations of this failure are discussed
  8. Chen, C.; Paul, R.J.; O'Keefe, B.: Fitting the Jigsaw of citation : information visualization in domain analysis (2001) 0.00
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    Date
    29. 9.2001 14:00:53
  9. Davis, P.M.; Cohen, S.A.: ¬The effect of the Web on undergraduate citation behavior 1996-1999 (2001) 0.00
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    Date
    29. 9.2001 14:01:09
  10. Frandsen, T.F.; Rousseau, R.: Article impact calculated over arbitrary periods (2005) 0.00
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    Date
    20. 3.2005 10:29:08
  11. Aksnes, D.W.: Citation rates and perceptions of scientific contribution (2006) 0.00
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    Date
    11. 2.2006 17:52:29
  12. González, L.; Campanario, J.M.: Structure of the impact factor of journals included in the Social Sciences Citation Index : citations from documents labeled "Editorial Material" (2007) 0.00
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    Date
    3. 3.2007 18:29:17
  13. Chan, H.C.; Kim, H.-W.; Tan, W.C.: Information systems citation patterns from International Conference on Information Systems articles (2006) 0.00
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    Date
    3. 1.2007 17:22:03
  14. H-Index auch im Web of Science (2008) 0.00
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    Date
    6. 4.2008 19:04:22
  15. Mingers, J.; Burrell, Q.L.: Modeling citation behavior in Management Science journals (2006) 0.00
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    Date
    26.12.2007 19:22:05
  16. Prime-Claverie, C.; Beigbeder, M.; Lafouge, T.: Transposition of the cocitation method with a view to classifying Web pages (2004) 0.00
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    Abstract
    The Web is a huge source of information, and one of the main problems facing users is finding documents which correspond to their requirements. Apart from the problem of thematic relevance, the documents retrieved by search engines do not always meet the users' expectations. The document may be too general, or conversely too specialized, or of a different type from what the user is looking for, and so forth. We think that adding metadata to pages can considerably improve the process of searching for information an the Web. This article presents a possible typology for Web sites and pages, as weIl as a method for propagating metadata values, based an the study of the Web graph and more specifically the method of cocitation in this graph.
  17. Case, D.O.; Higgins, G.M.: How can we investigate citation behavior? : A study of reasons for citing literature in communication (2000) 0.00
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    Date
    19. 5.2000 20:15:29
  18. Marion, L.S.; McCain, K.W.: Contrasting views of software engineering journals : author cocitation choices and indexer vocabulary assignments (2001) 0.00
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    Date
    29. 9.2001 14:01:01
  19. Hayer, L.: Lazarsfeld zitiert : eine bibliometrische Analyse (2008) 0.00
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    Date
    22. 6.2008 12:54:12
  20. Thelwall, M.; Vaughan, L.; Björneborn, L.: Webometrics (2004) 0.00
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    Abstract
    Webometrics, the quantitative study of Web-related phenomena, emerged from the realization that methods originally designed for bibliometric analysis of scientific journal article citation patterns could be applied to the Web, with commercial search engines providing the raw data. Almind and Ingwersen (1997) defined the field and gave it its name. Other pioneers included Rodriguez Gairin (1997) and Aguillo (1998). Larson (1996) undertook exploratory link structure analysis, as did Rousseau (1997). Webometrics encompasses research from fields beyond information science such as communication studies, statistical physics, and computer science. In this review we concentrate on link analysis, but also cover other aspects of webometrics, including Web log fle analysis. One theme that runs through this chapter is the messiness of Web data and the need for data cleansing heuristics. The uncontrolled Web creates numerous problems in the interpretation of results, for instance, from the automatic creation or replication of links. The loose connection between top-level domain specifications (e.g., com, edu, and org) and their actual content is also a frustrating problem. For example, many .com sites contain noncommercial content, although com is ostensibly the main commercial top-level domain. Indeed, a skeptical researcher could claim that obstacles of this kind are so great that all Web analyses lack value. As will be seen, one response to this view, a view shared by critics of evaluative bibliometrics, is to demonstrate that Web data correlate significantly with some non-Web data in order to prove that the Web data are not wholly random. A practical response has been to develop increasingly sophisticated data cleansing techniques and multiple data analysis methods.