Work

h-Index manipulation by undoing merges

Year: 2020

Type: article

Abstract: The h-index is an important bibliographic measure used to assess the performance of researchers. Dutiful researchers merge different versions of their articles in their Google Scholar profile even though this can decrease their h-index. In this article, we study the manipulation of the h-index by undoing such merges. In contrast to manipulation by merging articles, such manipulation is harder to detect. We present numerous results on computational complexity (from linear-time algorithms to parameterized computational hardness results) and empirically indicate that at least small improvements of the h-index by splitting merged articles are unfortunately easily achievable.

Source: Quantitative Science Studies

DOI: 10.1162/qss_a_00093

Authors: René van Bevern, Christian Komusiewicz, Hendrik Molter, Rolf Niedermeier, Manuel Sorge, Toby Walsh

Institutions: Novosibirsk State University, Philipps University of Marburg, Technische Universität Berlin, University of Warsaw

Language: English

Topic: scientometrics and bibliometrics research

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $800

Funders: Deutsche Forschungsgemeinschaft, Israel Science Foundation, H2020 European Research Council, FP7 People: Marie-Curie Actions