Crediting multi-authored papers to single authors

A fair assignment of credit for multi-authored publications is a long-standing issue in scientometrics. In the calculation of the $h$-index, for instance, all co-authors receive equal credit for a given publication, independent of a given author's contribution to the work or of the total number of co-authors. Several attempts have been made to distribute the credit in a more appropriate manner. In a recent paper, Hirsch has suggested a new way of credit assignment that is fundamentally different from the previous ones: All credit for a multi-author paper goes to a single author, the called ``$\alpha$-author'', defined as the person with the highest current $h$-index not the highest $h$-index at the time of the paper's publication) (J. E. Hirsch, Scientometrics 118, 673 (2019)). The collection of papers this author has received credit for as $\alpha$-author is then used to calculate a new index, $h_{\alpha}$, following the same recipe as for the usual $h$ index. The objective of this new assignment is not a fairer distribution of credit, but rather the determination of an altogether different property, the degree of a person's scientific leadership. We show that given the complex time dependence of $h$ for individual scientists, the approach of using the current $h$ value instead of the historic one is problematic, and we argue that it would be feasible to determine the $\alpha$-author at the time of the paper's publication instead. On the other hand, there are other practical considerations that make the calculation of the proposed $h_{\alpha}$ very difficult. As an alternative, we explore other ways of crediting papers to a single author in order to test early career achievement or scientific leadership.Comment: 6 pages, 4 figure

A proposal for a quantitative indicator of original research output

The use of quantitative indicators of scientific productivity seems now quite widespread for assessing researchers and research institutions. There is a general perception, however, that these indicators are not necessarily representative of the originality of the research carried out, being primarily indicative of a more or less prolific scientific activity and of the size of the targeted scientific subcommunity. We first discuss some of the drawbacks of the broadly adopted $h$-index and of the fact that it represents, in an average sense, an indicator derivable from the total number of citations. Then we propose an indicator which, although not immune from biases, seems more in line with the general expectations for quantifying what is typically considered original work. Qualitative arguments on how different indicators may shape the future of science are finally discussed.Comment: 6 pages, 4 figure

Exploiting citation networks for large-scale author name disambiguation

We present a novel algorithm and validation method for disambiguating author names in very large bibliographic data sets and apply it to the full Web of Science (WoS) citation index. Our algorithm relies only upon the author and citation graphs available for the whole period covered by the WoS. A pair-wise publication similarity metric, which is based on common co-authors, self-citations, shared references and citations, is established to perform a two-step agglomerative clustering that first connects individual papers and then merges similar clusters. This parameterized model is optimized using an h-index based recall measure, favoring the correct assignment of well-cited publications, and a name-initials-based precision using WoS metadata and cross-referenced Google Scholar profiles. Despite the use of limited metadata, we reach a recall of 87% and a precision of 88% with a preference for researchers with high h-index values. 47 million articles of WoS can be disambiguated on a single machine in less than a day. We develop an h-index distribution model, confirming that the prediction is in excellent agreement with the empirical data, and yielding insight into the utility of the h-index in real academic ranking scenarios.Comment: 14 pages, 5 figure