Letter to the EditorWhy Sirtes's claims (Sirtes, 2012) do not square with reality
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Cited by (5)
Quantifying and suppressing ranking bias in a large citation network
2017, Journal of InformetricsCitation Excerpt :Second, to quantify the bias by field of impact indicators, we assume that the fields are given by the Microsoft Academic's field classification scheme at its highest hierarchical level. In the literature, there is no general agreement on which field classification scheme should be used to classify papers and there is an entire stream of works investigating issues related to this (Adams, Gurney, & Jackson, 2008; Colliander & Ahlgren, 2011; Radicchi & Castellano, 2012a; Sirtes, 2012; Zitt, Ramanana-Rahary, & Bassecoulard, 2005). In particular, the choice of a suitable aggregation level has been shown to be delicate: by considering the most aggregate fields, heterogeneities in the subfields’ citation patterns might be hidden (Radicchi & Castellano, 2011; van Leeuwen & Medina, 2012) – this effect has been shown to be magnified when iterative ranking algorithms are used instead of citation count (Waltman, Yan, & van Eck, 2011).
A systematic empirical comparison of different approaches for normalizing citation impact indicators
2013, Journal of InformetricsCitation Excerpt :This is likely to introduce a bias in favor of this normalization approach. This problem was first pointed out by Sirtes (2012) in a comment on Radicchi and Castellano's (2012a) study (for the rejoinder, see Radicchi & Castellano, 2012b). As we have argued above, for a fairer comparison of the four normalization approaches that we study, we need to evaluate the approaches using a classification system different from the WoS subject categories.
Field normalization of scientometric indicators
2019, Springer HandbooksA systematic empirical comparison of different approaches for normalizing citation impact indicators
2013, Proceedings of ISSI 2013 - 14th International Society of Scientometrics and Informetrics Conference