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Actuality and Obsolescence of Scientific Literature

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  • Aurel Avramescu

Abstract

In order to explain the much increased citation frequency of recently published articles in respect to the normal exponential decay of older articles, the scientific information diffusion theory is used. The obsolescence rate results by combining the Gaussian normal distribution with the exponentially decreasing rate of the number of, articles with age. For recently published works supplementary adjustments are necessary: first, in order to have the citation number start at zero at the start of counting, and second, in order to reach the sixfold citation climax two years before it. All peculiarities exhibited by experimental survey data have been properly reproduced by making plausible assumptions suggested by the diffusion theory, such as preference of virtual authors to directly read recent articles, preference for verbal cross‐communication with colleagues, and increased preference for the newest articles in fields of personal interest. The latter preference is damped in the case of older articles since virtual authors prefer to consult the scientific community or review books. The results do confirm once more the reliability of the scientific information diffusion model.

Suggested Citation

  • Aurel Avramescu, 1979. "Actuality and Obsolescence of Scientific Literature," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 30(5), pages 296-303, September.
  • Handle: RePEc:bla:jamest:v:30:y:1979:i:5:p:296-303
    DOI: 10.1002/asi.4630300509
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    Cited by:

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    3. Zeng, Carl J. & Qi, Eric P. & Li, Simon S. & Stanley, H. Eugene & Ye, Fred Y., 2017. "Statistical characteristics of breakthrough discoveries in science using the metaphor of black and white swans," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 487(C), pages 40-46.
    4. Sangwal, Keshra, 2015. "On the growth dynamics of citations of articles by some Nobel Prize winners," Journal of Informetrics, Elsevier, vol. 9(3), pages 466-476.
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    8. Parolo, Pietro Della Briotta & Pan, Raj Kumar & Ghosh, Rumi & Huberman, Bernardo A. & Kaski, Kimmo & Fortunato, Santo, 2015. "Attention decay in science," Journal of Informetrics, Elsevier, vol. 9(4), pages 734-745.
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    16. Chong-Chen Wang & Yuh-Shan Ho, 2016. "Research trend of metal–organic frameworks: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 481-513, October.
    17. Onodera, Natsuo, 2016. "Properties of an index of citation durability of an article," Journal of Informetrics, Elsevier, vol. 10(4), pages 981-1004.
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    20. Lin Zhang & Wolfgang Glänzel, 2017. "A citation-based cross-disciplinary study on literature ageing: part II—diachronous aspects," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1559-1572, June.
    21. 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).
    22. Helena H. Zhang & Alesia A. Zuccala & Fred Y. Ye, 2019. "Tracing the ‘swan groups’ of physics and economics in the key publications of nobel laureates," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 425-436, April.
    23. Hamid Bouabid, 2011. "Revisiting citation aging: a model for citation distribution and life-cycle prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 199-211, July.
    24. Yunxue Cui & Yongzhen Wang & Xiaozhong Liu & Xianwen Wang & Xuhong Zhang, 2023. "Multidimensional scholarly citations: Characterizing and understanding scholars' citation behaviors," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 115-127, January.

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