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Determination and characteristics of the most visible authors in multi-author publications

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  • Li, Xian
  • Rousseau, Ronald
  • Jia, Tao

Abstract

Scientific collaboration is an effective approach to tackling complex challenges, with the most visible authors emerging as impactful contributors. This study analyzes over 21 million multi-authored papers in the social sciences, natural sciences, and engineering published between 1983 and 2012. We propose using Nonlinear Visibility Determination with a Modified Sigmoid Function (NVDMS) to identify the most visible authors and investigate their individual (byline order, academic age, gender), organizational (occupations), and national (affiliated country) characteristics across different years and team sizes. Our findings are: (1) From the individual point of view we find that the most visible authors in the social sciences are typically listed first or last, whereas in the natural sciences and engineering, they occupy middle positions. The proportion of those in middle positions increases with time and team sizes. The academic age of the most visible authors rises in the natural sciences and engineering but remains stable over time in the social sciences. While male authors continue to dominate, the gender imbalance decreases with time and team size. (2) From the organizational point of view, the most visible authors are affiliated with universities. A higher proportion of authors in the natural and engineering sciences are employed in the facility sector across various team sizes. (3) Finally, from a regional point of view, the most visible authors are predominantly from the U.S., U.K., Japan, and Germany, with a gradual shift toward eastern regions over time. Our findings offer insight into the structure of scientific teams and valuable implications for stakeholders to support promising scholars.

Suggested Citation

  • Li, Xian & Rousseau, Ronald & Jia, Tao, 2025. "Determination and characteristics of the most visible authors in multi-author publications," Journal of Informetrics, Elsevier, vol. 19(3).
  • Handle: RePEc:eee:infome:v:19:y:2025:i:3:s1751157725000471
    DOI: 10.1016/j.joi.2025.101683
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