Do mathematicians, economists and biomedical scientists trace large topics more strongly than physicists?. Issue 2 (May 2017)

• Record Type: Journal Article
• Authors: Li, Menghui; Yang, Liying; Zhang, Huina; Shen, Zhesi; Wu, Chensheng; Wu, Jinshan
• Is Part Of: Journal of informetrics. Volume 11:Issue 2(2017:Apr.)
• Page Start: 598
• Page End: 607
• Publication Date: 2017-05
• Subjects: Research choice -- Matthew effect -- Large topics; Library statistics -- Periodicals; Information science -- Statistical methods -- Periodicals; Bibliometrics -- Periodicals; Bibliothèques -- Statistiques -- Périodiques; Sciences de l'information -- Méthodes statistiques -- Périodiques; Bibliométrie -- Périodiques; 020.727
• DOI: 10.1016/j.joi.2017.04.004
• Languages: English
• ISSNs: 1751-1577

Abstract

Highlights: In choosing research topics, researchers are found to be largeness-tracing. Chinese scholars trace large topics more severely than scholars from the USA, Germany and Japan. Papers in top journals are relatively less largeness-tracing. There is a positive correlation between the degree of largeness tracing and the features of papers. Abstract: In this work, we extend our previous work on largeness tracing among physicists to other fields, namely mathematics, economics and biomedical science. Overall, the results confirm our previous discovery, indicating that scientists in all these fields trace large topics. Surprisingly, however, it seems that researchers in mathematics tend to be more likely to trace large topics than those in the other fields. We also find that on average, papers in top journals are less largeness-driven. We compare researchers from the USA, Germany, Japan and China and find that Chinese researchers exhibit consistently larger exponents, indicating that in all these fields, Chinese researchers trace large topics more strongly than others. Further correlation analyses between the degree of largeness tracing and the numbers of authors, affiliations and references per paper reveal positive correlations – papers with more authors, affiliations or references are likely to be more largeness-driven, with several interesting and noteworthy exceptions: in economics, papers with more references are not necessary more largeness-driven, and the same is true for papers with more authors in biomedical science. We believe that these empirical discoveries may be valuable to science policy-makers.