IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2603.01117.html

China leads scientific trends; the West launches new ones

Author

Listed:
  • Jeffrey W. Lockhart
  • Jamshid Sourati
  • Feng Shi
  • James Evans

Abstract

How nations shape the scientific frontier matters for technological competition, but standard metrics, including publication counts, citations, and disruption indices, look backward and fail to distinguish between fundamentally different leadership strategies. We develop and validate two forward-looking model-based measures and apply them to tens of millions of articles since 1990. The first embeds research pathways within an evolving hypergraph of concepts and scientists to identify leadership in emerging areas--work that anticipates where the scientific crowd is heading. The second embeds evolving samples of ideas and disciplines drawn upon in past research to identify leadership in surprising new directions as unexpected combinations become routine and science reorganizes around them. China became the global leader in emerging areas roughly a decade ago, well before it led in volume, reflecting a capacity to detect and amplify nascent consensus at scale. The United States and Europe show the opposite profile: declining emergence shares but persistent leadership in prescient work, especially research bridging disciplinary boundaries. These patterns replicate across databases, attribution methods, and strategic domains, including AI, biotechnology, energy, and semiconductors. Nations lead science by reading the landscape or by reshaping it, and the institutional requirements for each strategy lie in tension. The distribution of these strategies promises to shape the global structure of technological leadership for decades.

Suggested Citation

  • Jeffrey W. Lockhart & Jamshid Sourati & Feng Shi & James Evans, 2026. "China leads scientific trends; the West launches new ones," Papers 2603.01117, arXiv.org.
    • Handle: RePEc:arx:papers:2603.01117
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2603.01117
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jamshid Sourati & James A. Evans, 2023. "Accelerating science with human-aware artificial intelligence," Nature Human Behaviour, Nature, vol. 7(10), pages 1682-1696, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dawoon Jeong & Jeong-Dong Lee, 2025. "Mapping product development trajectories: product citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(8), pages 4667-4696, August.
    2. Joshua C. Yang & Damian Dailisan & Marcin Korecki & Carina I. Hausladen & Dirk Helbing, 2024. "LLM Voting: Human Choices and AI Collective Decision Making," Papers 2402.01766, arXiv.org, revised Aug 2024.
    3. Alex Jie Yang & Jianming Guo & Yujie Shi & Sanhong Deng & Star X. Zhao, 2025. "Repeat collaboration and scientific innovation: evidence from dynamic ego networks of Nobel laureates," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-12, December.
    4. Runhui Lin & Yalin Li & Wenchang Li & Ze Ji & Biting Li, 2025. "AI-enabled individual learning strategies and scientific innovation: a case from the field of computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(7), pages 3651-3677, July.
    5. Ningzi Li & Shiyang Lai & James Evans, 2025. "Big Data and the Computational Social Science of Entrepreneurship and Innovation," Papers 2505.08706, arXiv.org.
    6. Besiroglu, Tamay & Emery-Xu, Nicholas & Thompson, Neil, 2024. "Economic impacts of AI-augmented R&D," Research Policy, Elsevier, vol. 53(7).
    7. Marc Schmitt & Pantelis Koutroumpis, 2025. "Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures," Papers 2501.09025, arXiv.org, revised Jan 2025.
    8. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2603.01117. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.