Using an initial dataset consisting of 18.5 million distinct authors and 15 million distinct articles published in the period 2000–2016, which are classified into 29 broad scientific fields, we... Show moreUsing an initial dataset consisting of 18.5 million distinct authors and 15 million distinct articles published in the period 2000–2016, which are classified into 29 broad scientific fields, we search for regularities at the individual level for very productive authors with citation distributions of a certain size, and for the existence of a macro-micro relationship between the skewness of a scientific field citation distribution and the characteristics of the individual citation distributions of the authors belonging to the field. Our main results are the following three. Firstly, although the skewness of individual citation distributions varies greatly within each field, their average skewness is of a similar order of magnitude in all fields. Secondly, as in the previous literature, field citation distributions are highly skewed and the degree of skewness is very similar across fields. Thirdly, the skewness of field citation distributions is essentially explained in terms of the average skewness of individual authors, as well as individuals’ differences in mean citation rates and the number of publications per author. These results have important conceptual and practical consequences: to understand the skewness of field citation distributions at any aggregate level we must simply explain the skewness of the individual citation distributions of their very productive authors. Show less
Costas, R.; Perianes-Rodriguez, A.; Ruiz-Castillo, J. 2017
PurposeThe introduction of “altmetrics” as new tools to analyze scientific impact within the reward system of science has challenged the hegemony of citations as the predominant source for... Show morePurposeThe introduction of “altmetrics” as new tools to analyze scientific impact within the reward system of science has challenged the hegemony of citations as the predominant source for measuring scientific impact. Mendeley readership has been identified as one of the most important altmetric sources, with several features that are similar to citations. The purpose of this paper is to perform an in-depth analysis of the differences and similarities between the distributions of Mendeley readership and citations across fields.Design/methodology/approachThe authors analyze two issues by using in each case a common analytical framework for both metrics: the shape of the distributions of readership and citations, and the field normalization problem generated by differences in citation and readership practices across fields. In the first issue the authors use the characteristic scores and scales method, and in the second the measurement framework introduced in Crespo et al. (2013).FindingsThere are three main results. First, the citations and Mendeley readership distributions exhibit a strikingly similar degree of skewness in all fields. Second, the results on “exchange rates (ERs)” for Mendeley readership empirically supports the possibility of comparing readership counts across fields, as well as the field normalization of readership distributions using ERs as normalization factors. Third, field normalization using field mean readerships as normalization factors leads to comparably good results.Originality/valueThese findings open up challenging new questions, particularly regarding the possibility of obtaining conflicting results from field normalized citation and Mendeley readership indicators; this suggests the need for better determining the role of the two metrics in capturing scientific recognition. Show less
We study the problem of normalizing citation impact indicators for differences in citation practices across scientific fields. Normalization of citation impact indicators is usually done based on a... Show moreWe study the problem of normalizing citation impact indicators for differences in citation practices across scientific fields. Normalization of citation impact indicators is usually done based on a field classification system. In practice, the Web of Science journal subject categories are often used for this purpose. However, many of these subject categories have a quite broad scope and are not sufficiently homogeneous in terms of citation practices. As an alternative, we propose to work with algorithmically constructed classification systems. We construct these classification systems by performing a large-scale clustering of publications based on their citation relations. In our analysis, 12 classification systems are constructed, each at a different granularity level. The number of fields in these systems ranges from 390 to 73,205 in granularity levels 1–12. This contrasts with the 236 subject categories in the WoS classification system. Based on an investigation of some key characteristics of the 12 classification systems, we argue that working with a few thousand fields may be an optimal choice. We then study the effect of the choice of a classification system on the citation impact of the 500 universities included in the 2013 edition of the CWTS Leiden Ranking. We consider both the MNCS and the PPtop 10% indicator. Globally, for all the universities taken together citation impact indicators generally turn out to be relatively insensitive to the choice of a classification system. Nevertheless, for individual universities, we sometimes observe substantial differences between indicators normalized based on the journal subject categories and indicators normalized based on an appropriately chosen algorithmically constructed classification system. Show less
