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Alternative thoughts on uncitedness.

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Title: Alternative thoughts on uncitedness.
Authors: Burrell, Quentin L.1
Source: Journal of the American Society for Information Science & Technology. Jul2012, Vol. 63 Issue 7, p1466-1470. 5p. 3 Graphs.
Subjects: Scholarly method, Science, Citation analysis, Statistical models
Abstract: In a recent article, L. Egghe, R. Guns, and R. Rousseau () noted that in a study of some eminent scientists, many of them had a fair proportion of papers which were uncited and found this to be surprising. Here, we use the stochastic publication/citation model of Q. L. Burrell () to show that the result might in fact be expected. This brief communication is in the spirit of Q. L. Burrell (, ), showing that results that might at first sight seem to be surprising can in fact often be explainable in a stochastic framework. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: In a recent article, L. Egghe, R. Guns, and R. Rousseau () noted that in a study of some eminent scientists, many of them had a fair proportion of papers which were uncited and found this to be surprising. Here, we use the stochastic publication/citation model of Q. L. Burrell () to show that the result might in fact be expected. This brief communication is in the spirit of Q. L. Burrell (, ), showing that results that might at first sight seem to be surprising can in fact often be explainable in a stochastic framework. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/asi.22607
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