IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v18y2024i4s1751157724000841.html
   My bibliography  Save this article

Big Tech influence over AI research revisited: Memetic analysis of attribution of ideas to affiliation

Author

Listed:
  • Giziński, Stanisław
  • Kaczyńska, Paulina
  • Ruczyński, Hubert
  • Wiśnios, Emilia
  • Pieliński, Bartosz
  • Biecek, Przemysław
  • Sienkiewicz, Julian

Abstract

There exists a growing discourse around the domination of Big Tech on the landscape of artificial intelligence (AI) research, yet our comprehension of this phenomenon remains cursory. This paper aims to broaden and deepen our understanding of Big Tech's reach and power within AI research. It highlights the dominance not merely in terms of sheer publication volume but rather in the propagation of new ideas or memes. Current studies often oversimplify the concept of influence to the share of affiliations in academic papers, typically sourced from limited databases such as arXiv or specific academic conferences.

Suggested Citation

  • Giziński, Stanisław & Kaczyńska, Paulina & Ruczyński, Hubert & Wiśnios, Emilia & Pieliński, Bartosz & Biecek, Przemysław & Sienkiewicz, Julian, 2024. "Big Tech influence over AI research revisited: Memetic analysis of attribution of ideas to affiliation," Journal of Informetrics, Elsevier, vol. 18(4).
  • Handle: RePEc:eee:infome:v:18:y:2024:i:4:s1751157724000841
    DOI: 10.1016/j.joi.2024.101572
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157724000841
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.joi.2024.101572?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chao Min & Qingyu Chen & Erjia Yan & Yi Bu & Jianjun Sun, 2021. "Citation cascade and the evolution of topic relevance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(1), pages 110-127, January.
    2. Wagner, Caroline S. & Roessner, J. David & Bobb, Kamau & Klein, Julie Thompson & Boyack, Kevin W. & Keyton, Joann & Rafols, Ismael & Börner, Katy, 2011. "Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature," Journal of Informetrics, Elsevier, vol. 5(1), pages 14-26.
    3. Galang, Roberto Martin N., 2014. "Divergent diffusion: Understanding the interaction between institutions, firms, networks and knowledge in the international adoption of technology," Journal of World Business, Elsevier, vol. 49(4), pages 512-521.
    4. Wu, Chaojiang & Yan, Erjia & Hill, Chelsey, 2017. "Disciplinary knowledge diffusion in business research," Journal of Informetrics, Elsevier, vol. 11(2), pages 655-668.
    5. Xiaoling Sun & Kun Ding, 2018. "Identifying and tracking scientific and technological knowledge memes from citation networks of publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1735-1748, September.
    6. Wenyuan Liu & Andrea Nanetti & Siew Ann Cheong, 2017. "Knowledge evolution in physics research: An analysis of bibliographic coupling networks," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
    7. Mao, Jin & Liang, Zhentao & Cao, Yujie & Li, Gang, 2020. "Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes," Journal of Informetrics, Elsevier, vol. 14(4).
    8. Hargreaves Heap, Shaun P. & Parikh, Ashok, 2005. "The diffusion of ideas in the academy: A quantitative illustration from economics," Research Policy, Elsevier, vol. 34(10), pages 1619-1632, December.
    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. Mao, Jin & Liang, Zhentao & Cao, Yujie & Li, Gang, 2020. "Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes," Journal of Informetrics, Elsevier, vol. 14(4).
    2. Li, Xin & Wang, Yan, 2024. "A novel integrated approach for quantifying the convergence of disruptive technologies from science to technology," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    3. Shiyun Wang & Jin Mao & Yujie Cao & Gang Li, 2022. "Integrated knowledge content in an interdisciplinary field: identification, classification, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6581-6614, November.
    4. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.
    5. Sander Zwanenburg & Maryam Nakhoda & Peter Whigham, 2022. "Toward greater consistency and validity in measuring interdisciplinarity: a systematic and conceptual evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7769-7788, December.
    6. Stephen Carley & Alan L. Porter, 2012. "A forward diversity index," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 407-427, February.
    7. Abramo, Giovanni & D'Angelo, Ciriaco Andrea & Di Costa, Flavia, 2019. "Diversification versus specialization in scientific research: Which strategy pays off?," Technovation, Elsevier, vol. 82, pages 51-57.
    8. Ronald Rousseau, 2018. "The repeat rate: from Hirschman to Stirling," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 645-653, July.
    9. Su, Hsin-Ning & Moaniba, Igam M., 2017. "Investigating the dynamics of interdisciplinary evolution in technology developments," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 12-23.
    10. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    11. Junwan Liu & Xiaoyun Gong & Shuo Xu & Chenchen Huang, 2024. "Understanding the relationship between team diversity and the innovative performance in research teams using decision tree algorithms: evidence from artificial intelligence," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(12), pages 7805-7831, December.
    12. Seongkyoon Jeong & Jong-Chan Kim & Jae Young Choi, 2015. "Technology convergence: What developmental stage are we in?," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 841-871, September.
    13. Chaojiang Wu & Erjia Yan & Yongjun Zhu & Kai Li, 2021. "Gender imbalance in the productivity of funded projects: A study of the outputs of National Institutes of Health R01 grants," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(11), pages 1386-1399, November.
    14. Meijun Liu & Sijie Yang & Yi Bu & Ning Zhang, 2023. "Female early-career scientists have conducted less interdisciplinary research in the past six decades: evidence from doctoral theses," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    15. Zuo, Zhiya & Zhao, Kang, 2018. "The more multidisciplinary the better? – The prevalence and interdisciplinarity of research collaborations in multidisciplinary institutions," Journal of Informetrics, Elsevier, vol. 12(3), pages 736-756.
    16. Rafols, Ismael & Leydesdorff, Loet & O’Hare, Alice & Nightingale, Paul & Stirling, Andy, 2012. "How journal rankings can suppress interdisciplinary research: A comparison between Innovation Studies and Business & Management," Research Policy, Elsevier, vol. 41(7), pages 1262-1282.
    17. Andrés Pazmiño & Silvia Serrao-Neumann & Darryl Low Choy, 2018. "Towards Comprehensive Policy Integration for the Sustainability of Small Islands: A Landscape-Scale Planning Approach for the Galápagos Islands," Sustainability, MDPI, vol. 10(4), pages 1-29, April.
    18. Sándor Soós & Zsófia Vida & András Schubert, 2018. "Long-term trends in the multidisciplinarity of some typical natural and social sciences, and its implications on the SSH versus STM distinction," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 795-822, March.
    19. Ran Xu & Navid Ghaffarzadegan, 2018. "Neuroscience bridging scientific disciplines in health: Who builds the bridge, who pays for it?," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 1183-1204, November.
    20. Gibson, Elizabeth & Daim, Tugrul U. & Dabic, Marina, 2019. "Evaluating university industry collaborative research centers," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 181-202.

    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:eee:infome:v:18:y:2024:i:4:s1751157724000841. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    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.