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Long‐term variations in the aging of scientific literature: From exponential growth to steady‐state science (1900–2004)

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  • Vincent Larivière
  • Éric Archambault
  • Yves Gingras

Abstract

Despite a very large number of studies on the aging and obsolescence of scientific literature, no study has yet measured, over a very long time period, the changes in the rates at which scientific literature becomes obsolete. This article studies the evolution of the aging phenomenon and, in particular, how the age of cited literature has changed over more than 100 years of scientific activity. It shows that the average and median ages of cited literature have undergone several changes over the period. Specifically, both World War I and World War II had the effect of significantly increasing the age of the cited literature. The major finding of this article is that contrary to a widely held belief, the age of cited material has risen continuously since the mid‐1960s. In other words, during that period, researchers were relying on an increasingly old body of literature. Our data suggest that this phenomenon is a direct response to the steady‐state dynamics of modern science that followed its exponential growth; however, we also have observed that online preprint archives such as arXiv have had the opposite effect in some subfields.

Suggested Citation

  • Vincent Larivière & Éric Archambault & Yves Gingras, 2008. "Long‐term variations in the aging of scientific literature: From exponential growth to steady‐state science (1900–2004)," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(2), pages 288-296, January.
    • Handle: RePEc:bla:jamist:v:59:y:2008:i:2:p:288-296
    DOI: 10.1002/asi.20744
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    Cited by:

    1. Liang, Guoqiang & Hou, Haiyan & Ding, Ying & Hu, Zhigang, 2020. "Knowledge recency to the birth of Nobel Prize-winning articles: Gender, career stage, and country," Journal of Informetrics, Elsevier, vol. 14(3).
    2. Diana Hicks & Julia Melkers & Kimberley R. Isett, 2019. "A characterization of professional media and its links to research," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 827-843, May.
    3. Jordan Cambe & Sebastian Grauwin & Patrick Flandrin & Pablo Jensen, 2022. "A new clustering method to explore the dynamics of research communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4459-4482, August.
    4. Vincent Larivière & Stefanie Haustein & Philippe Mongeon, 2015. "The Oligopoly of Academic Publishers in the Digital Era," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-15, June.
    5. Minh Nguyen Quang & Tim Rogers & Jan Hofman & Ana B Lanham, 2019. "New framework for automated article selection applied to a literature review of Enhanced Biological Phosphorus Removal," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-42, May.
    6. Hao Liao & Rui Xiao & Giulio Cimini & Matúš Medo, 2014. "Network-Driven Reputation in Online Scientific Communities," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-18, December.
    7. Tanya Araújo & Elsa Fontainha, 2018. "Are scientific memes inherited differently from gendered authorship?," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 953-972, November.
    8. Ling-Ling Wu & Mu-Hsuan Huang & Ching-Yi Chen, 2012. "Citation patterns of the pre-web and web-prevalent environments: The moderating effects of domain knowledge," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(11), pages 2182-2194, November.
    9. Pablo Dorta-González & Emilio Gómez-Déniz, 2022. "Modeling the obsolescence of research literature in disciplinary journals through the age of their cited references," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 2901-2931, June.
    10. Colavizza, Giovanni & Franssen, Thomas & van Leeuwen, Thed, 2019. "An empirical investigation of the tribes and their territories: Are research specialisms rural and urban?," Journal of Informetrics, Elsevier, vol. 13(1), pages 105-117.
    11. Staša Milojević, 2012. "How Are Academic Age, Productivity and Collaboration Related to Citing Behavior of Researchers?," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-13, November.
    12. Pei-Shan Chi & Wolfgang Glänzel, 2022. "An article-based cross-disciplinary study of reference literature for indicator improvement," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7077-7089, December.
    13. Petersen, Alexander M. & Pan, Raj K. & Pammolli, Fabio & Fortunato, Santo, 2019. "Methods to account for citation inflation in research evaluation," Research Policy, Elsevier, vol. 48(7), pages 1855-1865.
    14. Shahzad, Murtuza & Alhoori, Hamed & Freedman, Reva & Rahman, Shaikh Abdul, 2022. "Quantifying the online long-term interest in research," Journal of Informetrics, Elsevier, vol. 16(2).
    15. Mengyi Sun & Jainabou Barry Danfa & Misha Teplitskiy, 2021. "Does double-blind peer-review reduce bias? Evidence from a top computer science conference," Papers 2101.02701, arXiv.org.

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