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Synthetic biology and governance research in China: a 40-year evolution

Author

Listed:
  • Li Tang

    (Fudan University)

  • Jennifer Kuzma

    (North Carolina State University)

  • Xi Zhang

    (Tianjin University)

  • Xinyu Song

    (Tianjin University)

  • Yin Li

    (Fudan University)

  • Hongxu Liu

    (Tongji University)

  • Guangyuan Hu

    (Shanghai University of Finance and Economics)

Abstract

The governance of emerging technologies has become a topic of global concern, not only for national competitiveness, but also for national security. Among other technologies, synthetic biology (SynBio) has been prioritized in the policy agenda of many countries; China is no exception. Unfortunately, despite the interconnectedness of governance practices and research development, few studies have investigated the current situation and development trajectory of this emerging dual use technology. To fill in this gap, this study focuses on China and investigates the pattern and evolution of its SynBio and related biosafety and biosecurity research published in both domestic and international databases. We find that despite its late entrance to the field, national government funding plays a critical role in China’s SynBio research. However, the funding ratio of SynBio as well as SynBio safety research is lower than China’s average when considering all fields. The structural topic model analysis reveals that the biological sciences dominate China’s SynBio research and slowly diffuse to other disciplines such as materials science, physics, and medicine, while perspectives from Chinese social scientists are barely recorded on the international academic stage. We also find little overlap of topics between China’s domestic and international output on SynBio and its safety research. Speculations and policy implications are discussed in the end.

Suggested Citation

  • Li Tang & Jennifer Kuzma & Xi Zhang & Xinyu Song & Yin Li & Hongxu Liu & Guangyuan Hu, 2023. "Synthetic biology and governance research in China: a 40-year evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5293-5310, September.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:9:d:10.1007_s11192-023-04789-0
    DOI: 10.1007/s11192-023-04789-0
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    References listed on IDEAS

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    1. Grajzl, Peter & Murrell, Peter, 2019. "Toward understanding 17th century English culture: A structural topic model of Francis Bacon's ideas," Journal of Comparative Economics, Elsevier, vol. 47(1), pages 111-135.
    2. Jennifer Kuzma & Todd Tanji, 2010. "Unpackaging synthetic biology: Identification of oversight policy problems and options," Regulation & Governance, John Wiley & Sons, vol. 4(1), pages 92-112, March.
    3. Sharma, Anuj & Rana, Nripendra P. & Nunkoo, Robin, 2021. "Fifty years of information management research: A conceptual structure analysis using structural topic modeling," International Journal of Information Management, Elsevier, vol. 58(C).
    4. Adèle Paul-Hus & Nadine Desrochers & Rodrigo Costas, 2016. "Characterization, description, and considerations for the use of funding acknowledgement data in Web of Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 167-182, July.
    5. Bai, Xiwen & Zhang, Xiunian & Li, Kevin X. & Zhou, Yaoming & Yuen, Kum Fai, 2021. "Research topics and trends in the maritime transport: A structural topic model," Transport Policy, Elsevier, vol. 102(C), pages 11-24.
    6. Benjamin Trump & Christopher Cummings & Jennifer Kuzma & Igor Linkov, 2018. "A decision analytic model to guide early‐stage government regulatory action: Applications for synthetic biology," Regulation & Governance, John Wiley & Sons, vol. 12(1), pages 88-100, March.
    7. Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
    8. Li Tang & Guangyuan Hu & Weishu Liu, 2017. "Funding acknowledgment analysis: Queries and caveats," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(3), pages 790-794, March.
    9. Sierra M. Brooks & Hal S. Alper, 2021. "Applications, challenges, and needs for employing synthetic biology beyond the lab," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    10. Philip Shapira & Seokbeom Kwon & Jan Youtie, 2017. "Tracking the emergence of synthetic biology," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1439-1469, September.
    11. Weishu Liu & Li Tang & Guangyuan Hu, 2020. "Funding information in Web of Science: an updated overview," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1509-1524, March.
    12. Margaret E. Roberts & Brandon M. Stewart & Edoardo M. Airoldi, 2016. "A Model of Text for Experimentation in the Social Sciences," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 988-1003, July.
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