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Conceptual structure and the growth of scientific knowledge

Nature Human Behaviour (2024)Cite this article

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Abstract

How does scientific knowledge grow? This question has occupied a central place in the philosophy of science, stimulating heated debates but yielding no clear consensus. Many explanations can be understood in terms of whether and how they view the expansion of knowledge as proceeding through the accretion of scientific concepts into larger conceptual structures. Here we examine these views empirically by analysing 2,605,224 papers spanning five decades from both the social sciences (Web of Science) and the physical sciences (American Physical Society). Using natural language processing techniques, we create semantic networks of concepts, wherein noun phrases become linked when used in the same paper abstract. We then detect the core/periphery structures of these networks, wherein core concepts are densely connected sets of highly central nodes and periphery concepts are sparsely connected nodes that are highly connected to the core. For both the social and physical sciences, we observe increasingly rigid conceptual cores accompanied by the proliferation of periphery concepts. Subsequently, we examine the relationship between conceptual structure and the growth of scientific knowledge, finding that scientific works are more innovative in fields with cores that have higher conceptual churn and with larger cores. Furthermore, scientific consensus is associated with reduced conceptual churn and fewer conceptual cores. Overall, our findings suggest that while the organization of scientific concepts is important for the growth of knowledge, the mechanisms vary across time.

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Fig. 1: Core/periphery measures over time for two fields.
Fig. 2: Conceptual structures over time.
Fig. 3: Schematic illustration of core/periphery measures.

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Data availability

The WoS data and the APS data are available from the Web of Science and the American Physical Society, respectively, but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available. If you are interested in accessing the WoS data, you can request access to the API through Clarivate, which requires an additional subscription or permission (https://clarivate.com/products/scientific-and-academic-research/research-discovery-and-workflow-solutions/webofscience-platform/web-of-science-core-collection/). For access to the APS data, you can request permission directly from their website (https://journals.aps.org/datasets/).

Code availability

The Python v.3 and Stata v.18 code we used to analyse and visualize the data for the current study are publicly available via Zenodo at https://doi.org/10.5281/zenodo.11533199 (ref. 49).

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Acknowledgements

We thank the National Science Foundation for financial support of work related to this project (grants no. 1829168 to R.J.F and no. 1932596 to R.J.F). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We also thank D. Hirschman, M. Park and Y. J. Kim for feedback on an earlier version of this work, and T. Gebhart for many helpful conversations and assistance with data and computation. Our work was presented as a poster at the 2nd Annual International Conference on the Science of Science and Innovation, as a poster at the 43rd Annual Meeting of the Cognitive Science Society, as a lightning talk at Networks 2021: A Joint Sunbelt and NetSci Conference, and as a poster at the 3rd North American Social Networks Conference.

Author information

Authors and Affiliations

  1. Institute for Complex Social Dynamics, Carnegie Mellon University, Pittsburgh, PA, USA

    Kara Kedrick

  2. The Coleridge Initiative, New York, NY, USA

    Ekaterina Levitskaya

  3. Carlson School of Management, University of Minnesota, Minneapolis, MN, USA

    Russell J. Funk

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  2. Ekaterina Levitskaya
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  3. Russell J. Funk
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Contributions

The study was conceptualized and designed by K.K., E.L. and R.J.F. The data analysis was conducted by K.K. and R.J.F. The manuscript was initially drafted by K.K., E.L. and R.J.F., with subsequent revisions made by K.K. and R.J.F.

Corresponding author

Correspondence to Russell J. Funk.

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The authors declare no competing interests.

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Peer review information

Nature Human Behaviour thanks Sadamori Kojaku, Marc Santolini and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Concepts extracted from the text of an abstract.

This figure shows an example abstract from the APS data; the highlighted text indicates single-word and multi-word noun phrases identified as concepts using our extraction algorithm. Reproduced with permission from ref. 50, American Physical Society.

Extended Data Table 1 Regression models of over time trends
Full size table
Extended Data Table 2 Regression models of scientific consensus
Full size table

Supplementary information

Supplementary Information

Supplementary Figs. 1–9 and Tables 1–3.

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Kedrick, K., Levitskaya, E. & Funk, R.J. Conceptual structure and the growth of scientific knowledge. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01957-x

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