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Predicting publication productivity for researchers: A piecewise Poisson model

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  • Xie, Zheng

Abstract

Predicting the publication productivity of research groups is a basic task for academic administrators and funding agencies. However, it is an elusive task due to diversity in researchers’ productivity patterns. This study proposed a model for the dynamics of the productivity, inspired by the distribution feature of the number of a researcher's publications. It is a piecewise Poisson model, analyzing and predicting the publication productivity of researchers by piecewise regression. The principle of the model is built on the explanation for the distribution feature as a result of an inhomogeneous Poisson process that can be approximated as a piecewise Poisson process. The principle is validated by applying it on the high-quality dblp dataset. The effectiveness of the model is tested on the dataset by fine fittings on the distribution of the number of publications for researchers, the evolutionary trend of their publication productivity, and the probability of producing publications. The model has the advantage of providing results in an unbiased way; thus would be useful for funding agencies that evaluate a vast number of applications provided by research groups with a quantitative index on publications.

Suggested Citation

  • Xie, Zheng, 2020. "Predicting publication productivity for researchers: A piecewise Poisson model," Journal of Informetrics, Elsevier, vol. 14(3).
    • Handle: RePEc:eee:infome:v:14:y:2020:i:3:s1751157719302676
    DOI: 10.1016/j.joi.2020.101065
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    Cited by:

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    3. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.

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