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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.01716 |
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| _version_ | 1866911353572163584 |
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| author | Dong, Haochen Qiao, Sun Mu, Yanping Liao, Lu Rodrigues, Diogo Sauerburger, Frank Bu, Yi Haunschild, Robin |
| author_facet | Dong, Haochen Qiao, Sun Mu, Yanping Liao, Lu Rodrigues, Diogo Sauerburger, Frank Bu, Yi Haunschild, Robin |
| contents | 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, librarians, and academic publishers in the fields of bibliometrics or scientometrics a valuable perspective for exploring I3 and I3/N within an inclusive database. This enables a more accurate and comprehensive understanding of disciplinary development and scientific progress. We advocate for piloting and validating this method in broader evaluation contexts to foster a more precise and diverse representation of scientific progress. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01716 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scilit with the Integrated Impact Indicator Assessment Dong, Haochen Qiao, Sun Mu, Yanping Liao, Lu Rodrigues, Diogo Sauerburger, Frank Bu, Yi Haunschild, Robin Digital Libraries 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, librarians, and academic publishers in the fields of bibliometrics or scientometrics a valuable perspective for exploring I3 and I3/N within an inclusive database. This enables a more accurate and comprehensive understanding of disciplinary development and scientific progress. We advocate for piloting and validating this method in broader evaluation contexts to foster a more precise and diverse representation of scientific progress. |
| title | Scilit with the Integrated Impact Indicator Assessment |
| topic | Digital Libraries |
| url | https://arxiv.org/abs/2601.01716 |