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Breakthrough paper indicator 2.0: can geographical diversity and interdisciplinarity improve the accuracy of outstanding papers prediction?

Author

Listed:
  • Ilya V. Ponomarev

    (Thomson Reuters)

  • Brian K. Lawton

    (Redtail Creek Software)

  • Duane E. Williams

    (Thomson Reuters)

  • Joshua D. Schnell

    (Thomson Reuters)

Abstract

We report progress on new developments in the breakthrough paper indicator, which allows early selection of a small group of publications which may become potential breakthrough candidates based on dynamics of publication citations and certain qualitative characteristics of citations. We used a quantitative approach to identify typical citation patterns of highly cited papers. Based on these analyses, we propose two forecasting models to select groups of breakthrough paper candidates that exceed high citation thresholds five years post-publication. Here we study whether interdisciplinarity in the subject categories or geographical diversity serve as possible measures to improve ranking of breakthrough paper candidates. We found that ranked geographical diversities of known breakthrough papers have equal or better ranks than corresponding citations ranks. This allows us to apply additional filtering for better identifications of breakthrough candidates. We studied several interdisciplinarity indices, including richness, Shannon index, Simpson index, and Rao-Stirling-Porter index. We did not find any correlations between citation ranks and ranked interdisciplinarity indices.

Suggested Citation

  • Ilya V. Ponomarev & Brian K. Lawton & Duane E. Williams & Joshua D. Schnell, 2014. "Breakthrough paper indicator 2.0: can geographical diversity and interdisciplinarity improve the accuracy of outstanding papers prediction?," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 755-765, September.
    • Handle: RePEc:spr:scient:v:100:y:2014:i:3:d:10.1007_s11192-014-1320-9
    DOI: 10.1007/s11192-014-1320-9
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Del Bo, Chiara F., 2016. "The rate of return to investment in R&D: The case of research infrastructures," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 26-37.
    2. Fenghua Wang & Ying Fan & An Zeng & Zengru Di, 2019. "Can we predict ESI highly cited publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 109-125, January.
    3. Simarjeet Singh & Nidhi Walia, 2022. "Momentum investing: a systematic literature review and bibliometric analysis," Management Review Quarterly, Springer, vol. 72(1), pages 87-113, February.
    4. Leon Guillén & Afcha Sergio & Chu Manuel, 2022. "Research on social responsibility of small and medium enterprises: a bibliometric analysis," Management Review Quarterly, Springer, vol. 72(3), pages 857-909, September.
    5. Dongqing Lyu & Kaile Gong & Xuanmin Ruan & Ying Cheng & Jiang Li, 2021. "Does research collaboration influence the “disruption” of articles? Evidence from neurosciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 287-303, January.
    6. Jielan Ding & Zhesi Shen & Per Ahlgren & Tobias Jeppsson & David Minguillo & Johan Lyhagen, 2021. "The link between ethnic diversity and scientific impact: the mediating effect of novelty and audience diversity," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7759-7810, September.
    7. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    8. Jos J. Winnink & Robert J. W. Tijssen & Anthony F. J. van Raan, 2016. "Theory‐changing breakthroughs in science: The impact of research teamwork on scientific discoveries," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1210-1223, May.

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