Scilit with the Integrated Impact Indicator Assessment
Haochen Dong, Sun Qiao, Yanping Mu, Lu Liao, Diogo Rodrigues, Frank Sauerburger, Yi Bu, Robin Haunschild

TL;DR
This paper evaluates the Scilit database and introduces the I3 and I3/N impact metrics, demonstrating their superiority over traditional metrics in assessing interdisciplinary journal impact with improved accuracy and fairness.
Contribution
It systematically assesses the Scilit I3/N metrics and compares them to traditional impact measures, highlighting their advantages in disciplinary fairness and robustness.
Findings
Scilit I3/N outperforms traditional impact metrics in coverage and fairness.
The new metrics provide a more accurate assessment of interdisciplinary journal impact.
The study offers a practical guide for adopting I3/N in bibliometric evaluations.
Abstract
In this study, we systematically elucidate the background and functionality of the Scilit database and evaluate the feasibility and advantages of the comprehensive impact metrics I3 and I3/N, introduced within the Scilit framework. Using a matched dataset of 17,816 journals, we conduct a comparative analysis of Scilit I3/N, Journal Impact Factor, and CiteScore for 2023 and 2024, covering descriptive statistics and distributional characteristics from both disciplinary and publisher perspectives. The comparison reveals that the Scilit I3 and I3/N framework significantly outperforms traditional mean-based metrics in terms of coverage, methodological robustness, and disciplinary fairness. It provides a more accurate, diagnosable, and responsible solution for interdisciplinary journal impact assessment. Our research serves as a "getting started guide" for Scilit, offering scholars,…
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Taxonomy
Topicsscientometrics and bibliometrics research · Academic Publishing and Open Access · Research Data Management Practices
