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A novel approach to predicting exceptional growth in research

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  • Richard Klavans
  • Kevin W Boyack
  • Dewey A Murdick

Abstract

The prediction of exceptional or surprising growth in research is an issue with deep roots and few practical solutions. In this study, we develop and validate a novel approach to forecasting growth in highly specific research communities. Each research community is represented by a cluster of papers. Multiple indicators were tested, and a composite indicator was created that predicts which research communities will experience exceptional growth over the next three years. The accuracy of this predictor was tested using hundreds of thousands of community-level forecasts and was found to exceed the performance benchmarks established in Intelligence Advanced Research Projects Activity’s (IARPA) Foresight Using Scientific Exposition (FUSE) program in six of nine major fields in science. Furthermore, 10 of 11 disciplines within the Computing Technologies field met the benchmarks. Specific detailed forecast examples are given and evaluated, and a critical evaluation of the forecasting approach is also provided.

Suggested Citation

  • Richard Klavans & Kevin W Boyack & Dewey A Murdick, 2020. "A novel approach to predicting exceptional growth in research," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-24, September.
    • Handle: RePEc:plo:pone00:0239177
    DOI: 10.1371/journal.pone.0239177
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    References listed on IDEAS

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    1. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
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    Cited by:

    1. Chavarro, Diego & Taborda, Jaime Andres Perez & Ávila, Alba, 2021. "Connecting brain and heart: artificial intelligence for sustainable development," SocArXiv gj5kr, Center for Open Science.
    2. Diego Chavarro & Jaime Andrés Perez-Taborda & Alba Ávila, 2022. "Connecting brain and heart: artificial intelligence for sustainable development," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7041-7060, December.
    3. Lu, Kun & Yang, Guancan & Wang, Xue, 2022. "Topics emerged in the biomedical field and their characteristics," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    4. Wenjie Wei & Hongxu Liu & Zhuanlan Sun, 2022. "Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4315-4333, August.
    5. Calof, Jonathan & Søilen, Klaus Solberg & Klavans, Richard & Abdulkader, Bisan & Moudni, Ismail El, 2022. "Understanding the structure, characteristics, and future of collective intelligence using local and global bibliometric analyses," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    6. Yuya Kajikawa, 2022. "Reframing evidence in evidence-based policy making and role of bibliometrics: toward transdisciplinary scientometric research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5571-5585, September.
    7. Gozuacik, Necip & Sakar, C. Okan & Ozcan, Sercan, 2023. "Technological forecasting based on estimation of word embedding matrix using LSTM networks," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

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