EconPapers    
Economics at your fingertips  
 

How to derive an advantage from the arbitrariness of the g-index

Michael Schreiber

Journal of Informetrics, 2013, vol. 7, issue 2, 555-561

Abstract: The definition of the g-index is as arbitrary as that of the h-index, because the threshold number g2 of citations to the g most cited papers can be modified by a prefactor at one's discretion, thus taking into account more or less of the highly cited publications within a dataset. In a case study I investigate the citation records of 26 physicists and show that the prefactor influences the ranking in terms of the generalized g-index less than for the generalized h-index. I propose specifically a prefactor of 2 for the g-index, because then the resulting values are of the same order of magnitude as for the common h-index. In this way one can avoid the disadvantage of the original g-index, namely that the values are usually substantially larger than for the h-index and thus the precision problem is substantially larger; while the advantages of the g-index over the h-index are kept. Like for the generalized h-index, also for the generalized g-index different prefactors might be more useful for investigations which concentrate only on top scientists with high citation frequencies or on junior researchers with small numbers of citations.

Keywords: g-index; Generalized g-index; h-index; Citation analysis; Evaluation (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1751157713000187
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:7:y:2013:i:2:p:555-561

DOI: 10.1016/j.joi.2013.02.003

Access Statistics for this article

Journal of Informetrics is currently edited by Leo Egghe

More articles in Journal of Informetrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:infome:v:7:y:2013:i:2:p:555-561