The dissociation of researchers from superstars through a new metric
F.T. Marchidan (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Hayley Hung – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Chenxu Hao – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
V. Agarwal – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
This study introduces a new metric for evaluating the disassociation between superstar and non-superstar researchers. Superstar researchers are defined as those in the top 0.1\% by h-index. Leveraging a large dataset, this paper analyzes the data and aims to flatten the discrepancy between superstars and non-superstars, in terms of innovation and popularity. Some authors that publish innovative papers and who haven't collaborated with superstars, tend to be left in the shadows, compared to the ones that have collaborated with superstars from an early stage. The new metric indicates the disassociation between such authors, by factoring in certain parameters that were put into perspective with the help of a Multiple Linear Regression model. The findings reveal significant differences in dissociation scores between researchers and superstar researchers, offering new insights into the dynamics of academic innovation and collaboration. This metric provides a robust tool to identify where an author stands in terms of dissociation and what needs to be done to diminish the discrepancy.