TY - JOUR
T1 - A non-linear index to evaluate a journal's scientific impact
AU - Papavlasopoulos, S.
AU - Poulos, M.
AU - Korfiatis, N.
AU - Bokos, G.
PY - 2010/6/1
Y1 - 2010/6/1
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 - Bibliometrics
KW - Semantic classification
KW - Elman neural network
KW - Impact factor
UR - http://www.scopus.com/inward/record.url?scp=77649336412&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2010.01.018
DO - 10.1016/j.ins.2010.01.018
M3 - Article
AN - SCOPUS:77649336412
VL - 180
SP - 2156
EP - 2175
JO - Information Sciences
JF - Information Sciences
SN - 0020-0255
IS - 11
ER -