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Evolution and impact of the science of science: from theoretical analysis to digital-AI driven research

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Listed:
  • Jianhua Hou

    (Panyu District)

  • Bili Zheng

    (Panyu District)

  • Hao Li

    (Panyu District)

  • Wenjing Li

    (Panyu District)

Abstract

The Science of Science (SoS) examines the mechanisms driving the development and societal role of science, evolving from its sociological roots into a data-driven discipline. This paper traces the progression of SoS from its early focus on the social functions of science to the current era, characterized by large-scale quantitative analysis and AI-driven methodologies. Scientometrics, a key branch of SoS, has utilized statistical methods and citation analysis to understand scientific growth and knowledge diffusion. With the rise of big data and complex network theory, SoS has transitioned toward more refined analyses, leveraging artificial intelligence (AI) for predictive modeling, sentiment annotation, and entity extraction. This paper explores the application of AI in SoS, highlighting its role as a surrogate, quant, and arbiter in advancing data processing, data analysis and peer review. The integration of AI has ushered in a new paradigm for SoS, enhancing its predictive accuracy and providing deeper insights into the internal dynamics of science and its impact on society.

Suggested Citation

  • Jianhua Hou & Bili Zheng & Hao Li & Wenjing Li, 2025. "Evolution and impact of the science of science: from theoretical analysis to digital-AI driven research," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-9, December.
  • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04617-1
    DOI: 10.1057/s41599-025-04617-1
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    as
    1. An Zeng & Zhesi Shen & Jianlin Zhou & Ying Fan & Zengru Di & Yougui Wang & H. Eugene Stanley & Shlomo Havlin, 2019. "Increasing trend of scientists to switch between topics," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    2. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    3. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    4. Tohalino, Jorge A.V. & Amancio, Diego R., 2022. "On predicting research grants productivity via machine learning," Journal of Informetrics, Elsevier, vol. 16(2).
    5. Yang Wang & Benjamin F. Jones & Dashun Wang, 2019. "Early-career setback and future career impact," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    6. Cassidy R. Sugimoto, 2021. "Scientific success by numbers," Nature, Nature, vol. 593(7857), pages 30-31, May.
    7. van Eck, Nees Jan & Waltman, Ludo, 2014. "CitNetExplorer: A new software tool for analyzing and visualizing citation networks," Journal of Informetrics, Elsevier, vol. 8(4), pages 802-823.
    8. Yi Bu & Ying Ding & Xingkun Liang & Dakota S. Murray, 2018. "Understanding persistent scientific collaboration," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(3), pages 438-448, March.
    9. Tong Zeng & Daniel E. Acuna, 2020. "Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable models," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 399-428, July.
    10. Alessandro Checco & Lorenzo Bracciale & Pierpaolo Loreti & Stephen Pinfield & Giuseppe Bianchi, 2021. "AI-assisted peer review," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-11, December.
    11. Tao Jia & Dashun Wang & Boleslaw K. Szymanski, 2017. "Quantifying patterns of research-interest evolution," Nature Human Behaviour, Nature, vol. 1(4), pages 1-7, April.
    12. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    13. Sehrish Iqbal & Saeed-Ul Hassan & Naif Radi Aljohani & Salem Alelyani & Raheel Nawaz & Lutz Bornmann, 2021. "A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6551-6599, August.
    14. Lisa Messeri & M. J. Crockett, 2024. "Artificial intelligence and illusions of understanding in scientific research," Nature, Nature, vol. 627(8002), pages 49-58, March.
    15. Parolo, Pietro Della Briotta & Pan, Raj Kumar & Ghosh, Rumi & Huberman, Bernardo A. & Kaski, Kimmo & Fortunato, Santo, 2015. "Attention decay in science," Journal of Informetrics, Elsevier, vol. 9(4), pages 734-745.
    16. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    17. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    18. Indra Budi & Yaniasih Yaniasih, 2023. "Understanding the meanings of citations using sentiment, role, and citation function classifications," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 735-759, January.
    19. Bo Peng & Ye Wei & Yu Qin & Jiabao Dai & Yue Li & Aobo Liu & Yun Tian & Liuliu Han & Yufeng Zheng & Peng Wen, 2023. "Machine learning-enabled constrained multi-objective design of architected materials," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    20. Kong, Ling & Zhang, Wei & Hu, Haotian & Liang, Zhu & Han, Yonggang & Wang, Dongbo & Song, Min, 2024. "Transdisciplinary fine-grained citation content analysis: A multi-task learning perspective for citation aspect and sentiment classification," Journal of Informetrics, Elsevier, vol. 18(3).
    21. Alexander J. Gates & Qing Ke & Onur Varol & Albert-László Barabási, 2019. "Nature’s reach: narrow work has broad impact," Nature, Nature, vol. 575(7781), pages 32-34, November.
    22. Yiling Lin & Carl Benedikt Frey & Lingfei Wu, 2022. "Remote Collaboration Fuses Fewer Breakthrough Ideas," Papers 2206.01878, arXiv.org, revised Oct 2023.
    23. Zhao, Zhenyue & Bu, Yi & Kang, Lele & Min, Chao & Bian, Yiyang & Tang, Li & Li, Jiang, 2020. "An investigation of the relationship between scientists’ mobility to/from China and their research performance," Journal of Informetrics, Elsevier, vol. 14(2).
    24. Jiang, Hongxun & Fan, Shaokun & Zhang, Nan & Zhu, Bin, 2023. "Deep learning for predicting patent application outcome: The fusion of text and network embeddings," Journal of Informetrics, Elsevier, vol. 17(2).
    25. Yuxian Liu & Ronald Rousseau, 2010. "Knowledge diffusion through publications and citations: A case study using ESI-fields as unit of diffusion," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(2), pages 340-351, February.
    26. Lu Liu & Yang Wang & Roberta Sinatra & C. Lee Giles & Chaoming Song & Dashun Wang, 2018. "Hot streaks in artistic, cultural, and scientific careers," Nature, Nature, vol. 559(7714), pages 396-399, July.
    27. Yiling Lin & Carl Benedikt Frey & Lingfei Wu, 2023. "Remote collaboration fuses fewer breakthrough ideas," Nature, Nature, vol. 623(7989), pages 987-991, November.
    28. Lu Liu & Nima Dehmamy & Jillian Chown & C. Lee Giles & Dashun Wang, 2021. "Understanding the onset of hot streaks across artistic, cultural, and scientific careers," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    29. Yordan Kalmukov, 2020. "An algorithm for automatic assignment of reviewers to papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1811-1850, September.
    30. Yuxian Liu & Ronald Rousseau, 2010. "Knowledge diffusion through publications and citations: A case study using ESI‐fields as unit of diffusion," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(2), pages 340-351, February.
    31. Akella, Akhil Pandey & Alhoori, Hamed & Kondamudi, Pavan Ravikanth & Freeman, Cole & Zhou, Haiming, 2021. "Early indicators of scientific impact: Predicting citations with altmetrics," Journal of Informetrics, Elsevier, vol. 15(2).
    32. Yi Bu & Dakota S. Murray & Ying Ding & Yong Huang & Yiming Zhao, 2018. "Measuring the stability of scientific collaboration," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 463-479, February.
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