A non-linear index to evaluate a journal's scientific impact

Research output: Contribution to journalJournal articlepeer-review

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A non-linear index to evaluate a journal's scientific impact. / Papavlasopoulos, Sozon; Poulos, Marios; Korfiatis, Nikolaos; Bokos, George.

In: Information Sciences, Vol. 180, No. 11, 2010, p. 2156-2175.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Papavlasopoulos, S, Poulos, M, Korfiatis, N & Bokos, G 2010, 'A non-linear index to evaluate a journal's scientific impact', Information Sciences, vol. 180, no. 11, pp. 2156-2175. https://doi.org/10.1016/j.ins.2010.01.018

APA

Papavlasopoulos, S., Poulos, M., Korfiatis, N., & Bokos, G. (2010). A non-linear index to evaluate a journal's scientific impact. Information Sciences, 180(11), 2156-2175. https://doi.org/10.1016/j.ins.2010.01.018

Vancouver

Papavlasopoulos S, Poulos M, Korfiatis N, Bokos G. A non-linear index to evaluate a journal's scientific impact. Information Sciences. 2010;180(11):2156-2175. https://doi.org/10.1016/j.ins.2010.01.018

Author

Papavlasopoulos, Sozon ; Poulos, Marios ; Korfiatis, Nikolaos ; Bokos, George. / A non-linear index to evaluate a journal's scientific impact. In: Information Sciences. 2010 ; Vol. 180, No. 11. pp. 2156-2175.

Bibtex

@article{5ee11590f7cd11dfb6d2000ea68e967b,
title = "A non-linear index to evaluate a journal's scientific impact",
abstract = "The purpose of this study is to define a bibliometric indicator of the scientific impact of a journal, which combines objectivity with the ability to bridge many different bibliometric factors and in particular the side factors presented along with celebrated ISI impact factor. The particular goal is to determine a standard threshold value in which an independent self-organizing system will decide the correlation between this value and the impact factor of a journal. We name this factor {"}Cited Distance Factor (CDF){"} and it is extracted via a well-fitted, recurrent Elman neural network. For a case study of this implementation we used a dataset of all journals of cell biology, ranking them according to the impact factor from the Web of Science Database and then comparing the rank according to the cited distance. For clarity reasons we also compare the cited distance factor with already known measures and especially with the recently introduced eigenfactor of the institute of scientific information (ISI).",
keywords = "Faculty of Social Sciences, bibliometrics, semantic classification, Elman neural network, impact factor",
author = "Sozon Papavlasopoulos and Marios Poulos and Nikolaos Korfiatis and George Bokos",
year = "2010",
doi = "10.1016/j.ins.2010.01.018",
language = "English",
volume = "180",
pages = "2156--2175",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier",
number = "11",

}

RIS

TY - JOUR

T1 - A non-linear index to evaluate a journal's scientific impact

AU - Papavlasopoulos, Sozon

AU - Poulos, Marios

AU - Korfiatis, Nikolaos

AU - Bokos, George

PY - 2010

Y1 - 2010

N2 - The purpose of this study is to define a bibliometric indicator of the scientific impact of a journal, which combines objectivity with the ability to bridge many different bibliometric factors and in particular the side factors presented along with celebrated ISI impact factor. The particular goal is to determine a standard threshold value in which an independent self-organizing system will decide the correlation between this value and the impact factor of a journal. We name this factor "Cited Distance Factor (CDF)" and it is extracted via a well-fitted, recurrent Elman neural network. For a case study of this implementation we used a dataset of all journals of cell biology, ranking them according to the impact factor from the Web of Science Database and then comparing the rank according to the cited distance. For clarity reasons we also compare the cited distance factor with already known measures and especially with the recently introduced eigenfactor of the institute of scientific information (ISI).

AB - The purpose of this study is to define a bibliometric indicator of the scientific impact of a journal, which combines objectivity with the ability to bridge many different bibliometric factors and in particular the side factors presented along with celebrated ISI impact factor. The particular goal is to determine a standard threshold value in which an independent self-organizing system will decide the correlation between this value and the impact factor of a journal. We name this factor "Cited Distance Factor (CDF)" and it is extracted via a well-fitted, recurrent Elman neural network. For a case study of this implementation we used a dataset of all journals of cell biology, ranking them according to the impact factor from the Web of Science Database and then comparing the rank according to the cited distance. For clarity reasons we also compare the cited distance factor with already known measures and especially with the recently introduced eigenfactor of the institute of scientific information (ISI).

KW - Faculty of Social Sciences

KW - bibliometrics

KW - semantic classification

KW - Elman neural network

KW - impact factor

U2 - 10.1016/j.ins.2010.01.018

DO - 10.1016/j.ins.2010.01.018

M3 - Journal article

VL - 180

SP - 2156

EP - 2175

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

IS - 11

ER -

ID: 23347925