Search (974 results, page 1 of 49)

  • × theme_ss:"Informetrie"
  1. Bookstein, A.: Informetric distributions : I. Unified overview (1990) 0.06
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    Date
    17. 2.1997 18:25:04
    22. 7.2006 18:55:29
  2. Bookstein, A.: Informetric distributions : II. Resilience to ambiguity (1990) 0.06
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    Date
    17. 2.1997 18:25:04
    22. 7.2006 18:55:55
  3. Lewison, G.: ¬The work of the Bibliometrics Research Group (City University) and associates (2005) 0.05
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    Date
    20. 1.2007 17:02:22
  4. Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014) 0.05
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    Date
    17. 2.1997 18:25:04
    18. 3.2014 19:13:22
  5. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.04
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    Date
    17. 2.1997 18:25:04
    22. 8.2014 17:05:18
  6. Scholarly metrics under the microscope : from citation analysis to academic auditing (2015) 0.03
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    Date
    22. 1.2017 17:12:50
  7. Raan, A.F.J. van: Statistical properties of bibliometric indicators : research group indicator distributions and correlations (2006) 0.03
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    Date
    17. 2.1997 18:25:04
    22. 7.2006 16:20:22
  8. Larivière, V.; Gingras, Y.; Archambault, E.: ¬The decline in the concentration of citations, 1900-2007 (2009) 0.03
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    Date
    17. 2.1997 18:25:04
    22. 3.2009 19:22:35
  9. Albarrán, P.; Ruiz-Castillo, J.: References made and citations received by scientific articles (2011) 0.03
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    Abstract
    This article studies massive evidence about references made and citations received after a 5-year citation window by 3.7 million articles published in 1998 to 2002 in 22 scientific fields. We find that the distributions of references made and citations received share a number of basic features across sciences. Reference distributions are rather skewed to the right while citation distributions are even more highly skewed: The mean is about 20 percentage points to the right of the median, and articles with a remarkable or an outstanding number of citations represent about 9% of the total. Moreover, the existence of a power law representing the upper tail of citation distributions cannot be rejected in 17 fields whose articles represent 74.7% of the total. Contrary to the evidence in other contexts, the value of the scale parameter is above 3.5 in 13 of the 17 cases. Finally, power laws are typically small, but capture a considerable proportion of the total citations received.
    Date
    17. 2.1997 18:25:04
  10. Crespo, J.A.; Herranz, N.; Li, Y.; Ruiz-Castillo, J.: ¬The effect on citation inequality of differences in citation practices at the web of science subject category level (2014) 0.03
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    Abstract
    This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.
    Date
    17. 2.1997 18:25:04
    16. 6.2014 19:17:08
  11. Neth, M.: Citation analysis and the Web (1998) 0.03
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    Date
    10. 1.1999 16:22:37
    Source
    Art documentation. 17(1998) no.1, S.29-33
  12. Burrell, Q.L.: Predicting future citation behavior (2003) 0.03
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    Date
    17. 2.1997 18:25:04
    29. 3.2003 19:22:48
  13. Campanario, J.M.: Large increases and decreases in journal impact factors in only one year : the effect of journal self-citations (2011) 0.03
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    Date
    17. 2.1997 18:25:04
    22. 1.2011 12:53:00
  14. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.03
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    Date
    17. 2.1997 18:25:04
    22. 1.2011 13:02:21
  15. Schlögl, C.: Internationale Sichtbarkeit der europäischen und insbesondere der deutschsprachigen Informationswissenschaft (2013) 0.03
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    Date
    13. 3.2012 19:17:06
    22. 3.2013 14:04:09
  16. Vieira, E.S.; Cabral, J.A.S.; Gomes, J.A.N.F.: Definition of a model based on bibliometric indicators for assessing applicants to academic positions (2014) 0.03
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    Date
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  17. Manley, S.: Letters to the editor and the race for publication metrics (2022) 0.03
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  18. Chan, H.C.; Kim, H.-W.; Tan, W.C.: Information systems citation patterns from International Conference on Information Systems articles (2006) 0.03
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  19. Zhang, Y.; Jansen, B.J.; Spink, A.: Identification of factors predicting clickthrough in Web searching using neural network analysis (2009) 0.03
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  20. Bornmann, L.: How to analyze percentile citation impact data meaningfully in bibliometrics : the statistical analysis of distributions, percentile rank classes, and top-cited papers (2013) 0.03
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    Date
    17. 2.1997 18:25:04
    22. 3.2013 19:44:17

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