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Quantifying the cognitive extent of science

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  • Milojević, Staša

Abstract

While the modern science is characterized by an exponential growth in scientific literature, the increase in publication volume clearly does not reflect the expansion of the cognitive boundaries of science. Nevertheless, most of the metrics for assessing the vitality of science or for making funding and policy decisions are based on productivity. Similarly, the increasing level of knowledge production by large science teams, whose results often enjoy greater visibility, does not necessarily mean that “big science” leads to cognitive expansion. Here we present a novel, big-data method to quantify the extents of cognitive domains of different bodies of scientific literature independently from publication volume, and apply it to 20 million articles published over 60–130 years in physics, astronomy, and biomedicine. The method is based on the lexical diversity of titles of fixed quotas of research articles. Owing to large size of quotas, the method overcomes the inherent stochasticity of article titles to achieve <1% precision. We show that the periods of cognitive growth do not necessarily coincide with the trends in publication volume. Furthermore, we show that the articles produced by larger teams cover significantly smaller cognitive territory than (the same quota of) articles from smaller teams. Our findings provide a new perspective on the role of small teams and individual researchers in expanding the cognitive boundaries of science. The proposed method of quantifying the extent of the cognitive territory can also be applied to study many other aspects of ‘science of science.’

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  • Milojević, Staša, 2015. "Quantifying the cognitive extent of science," Journal of Informetrics, Elsevier, vol. 9(4), pages 962-973.
    • Handle: RePEc:eee:infome:v:9:y:2015:i:4:p:962-973
    DOI: 10.1016/j.joi.2015.10.005
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    Cited by:

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    2. Martin Ricker, 2017. "Letter to the Editor: About the quality and impact of scientific articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1851-1855, June.
    3. Haeussler, Carolin & Sauermann, Henry, 2020. "Division of labor in collaborative knowledge production: The role of team size and interdisciplinarity," Research Policy, Elsevier, vol. 49(6).
    4. Pierre Pelletier & Kevin Wirtz, 2023. "Sails and Anchors: The Complementarity of Exploratory and Exploitative Scientists in Knowledge Creation," Papers 2312.10476, arXiv.org.
    5. Sun, Xiaoling & Ding, Kun & Lin, Yuan, 2016. "Mapping the evolution of scientific fields based on cross-field authors," Journal of Informetrics, Elsevier, vol. 10(3), pages 750-761.
    6. Salva Duran-Nebreda & Michael J. O’Brien & R. Alexander Bentley & Sergi Valverde, 2022. "Dilution of expertise in the rise and fall of collective innovation," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-10, December.
    7. Carlos Olmeda-Gómez & Carlos Romá-Mateo & Maria-Antonia Ovalle-Perandones, 2019. "Overview of trends in global epigenetic research (2009–2017)," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1545-1574, June.
    8. Wu, Lingfei & Kittur, Aniket & Youn, Hyejin & Milojević, Staša & Leahey, Erin & Fiore, Stephen M. & Ahn, Yong-Yeol, 2022. "Metrics and mechanisms: Measuring the unmeasurable in the science of science," Journal of Informetrics, Elsevier, vol. 16(2).
    9. Michael Park & Erin Leahey & Russell Funk, 2021. "The decline of disruptive science and technology," Papers 2106.11184, arXiv.org, revised Jul 2022.
    10. Diego Kozlowski & Jennifer Dusdal & Jun Pang & Andreas Zilian, 2021. "Semantic and relational spaces in science of science: deep learning models for article vectorisation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5881-5910, July.
    11. Xuechun Xiang & Jing Li, 2020. "A diachronic comparative study of research article titles in linguistics and literature journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 847-866, February.
    12. Woo, Seokkyun & Youtie, Jan & Ott, Ingrid & Scheu, Fenja, 2021. "Understanding the long-term emergence of autonomous vehicles technologies," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
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    14. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.

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