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Parsimonious citer-based measures: The artificial intelligence domain as a case study.

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Title: Parsimonious citer-based measures: The artificial intelligence domain as a case study.
Authors: Rokach, Lior1 liorrk@bgu.ac.il, Mitra, Prasenjit2 pmitra@ist.psu.edu
Source: Journal of the American Society for Information Science & Technology. Sep2013, Vol. 64 Issue 9, p1951-1959. 9p. 1 Chart, 7 Graphs.
Subjects: Artificial intelligence, Confidence intervals, Research funding, Scientists, Serial publications, Citation analysis
Abstract: This article presents a new Parsimonious Citer- Based Measure for assessing the quality of academic papers. This new measure is parsimonious as it looks for the smallest set of citing authors (citers) who have read a certain paper. The Parsimonious Citer- Based Measure aims to address potential distortion in the values of existing citer-based measures. These distortions occur because of various factors, such as the practice of hyperauthorship. This new measure is empirically compared with existing measures, such as the number of citers and the number of citations in the field of artificial intelligence ( AI). The results show that the new measure is highly correlated with those two measures. However, the new measure is more robust against citation manipulations and better differentiates between prominent and nonprominent AI researchers than the above-mentioned measures. [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: <searchLink fieldCode="AR" term="%22Rokach%2C+Lior%22">Rokach, Lior</searchLink><relatesTo>1</relatesTo><i> liorrk@bgu.ac.il</i><br /><searchLink fieldCode="AR" term="%22Mitra%2C+Prasenjit%22">Mitra, Prasenjit</searchLink><relatesTo>2</relatesTo><i> pmitra@ist.psu.edu</i>
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Scientists%22">Scientists</searchLink><br /><searchLink fieldCode="DE" term="%22Serial+publications%22">Serial publications</searchLink><br /><searchLink fieldCode="DE" term="%22Citation+analysis%22">Citation analysis</searchLink>
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  Data: This article presents a new Parsimonious Citer- Based Measure for assessing the quality of academic papers. This new measure is parsimonious as it looks for the smallest set of citing authors (citers) who have read a certain paper. The Parsimonious Citer- Based Measure aims to address potential distortion in the values of existing citer-based measures. These distortions occur because of various factors, such as the practice of hyperauthorship. This new measure is empirically compared with existing measures, such as the number of citers and the number of citations in the field of artificial intelligence ( AI). The results show that the new measure is highly correlated with those two measures. However, the new measure is more robust against citation manipulations and better differentiates between prominent and nonprominent AI researchers than the above-mentioned measures. [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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      – SubjectFull: Confidence intervals
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      – SubjectFull: Research funding
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