Learning nuanced cross-disciplinary citation metric normalization using the hierarchical Dirichlet process on big scholarly data

Citation counts have long been used in academia as a way of measuring, inter alia, the importance of journals, quantifying the significance and the impact of a researcher's body of work, and allocating funding for individuals and departments. For example, the h-index proposed by Hirsch is one of the most popular metrics that utilizes citation analysis to determine an individual's research impact. Among many issues, one of the pitfalls of citation metrics is the unfairness which emerges when comparisons are made between researchers in different fields. The algorithm we described in the present paper learns evidence based, nuanced, and probabilistic representations of academic fields, and uses data collected by crawling Google Scholar to perform field of study based normalization of citation based impact metrics such as the h-index.Postprin

Learning nuanced cross-disciplinary citation metric normalization using the hierarchical Dirichlet process on big scholarly data

Citation counts have long been used in academia as a way of measuring, inter alia, the importance of journals, quantifying the significance and the impact of a researcher's body of work, and allocating funding for individuals and departments. For example, the h-index proposed by Hirsch is one of the most popular metrics that utilizes citation analysis to determine an individual's research impact. Among many issues, one of the pitfalls of citation metrics is the unfairness which emerges when comparisons are made between researchers in different fields. The algorithm we described in the present paper learns evidence based, nuanced, and probabilistic representations of academic fields, and uses data collected by crawling Google Scholar to perform field of study based normalization of citation based impact metrics such as the h-index