An investigation on the skewness patterns and fractal nature of research productivity distributions at field and discipline level

Citation data:

Journal of Informetrics, ISSN: 1751-1577, Vol: 11, Issue: 1, Page: 324-335

Publication Year:
2017
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Repository URL:
http://hdl.handle.net/2108/166674; http://www.journals.elsevier.com/journal-of-informetrics/; http://hdl.handle.net/2108/121364; http://hdl.handle.net/2108/166680; http://hdl.handle.net/2108/180167; http://hdl.handle.net/2108/188949; http://hdl.handle.net/2108/188947; http://hdl.handle.net/2108/180162
DOI:
10.1016/j.joi.2017.02.001; 10.1016/j.joi.2017.07.006; 10.1016/j.joi.2015.09.001; 10.1016/j.joi.2016.07.005; 10.1016/j.joi.2016.07.004; 10.1016/j.joi.2017.06.003; 10.1016/j.joi.2016.12.003
Author(s):
Giovanni Abramo; Ciriaco Andrea D’Angelo; Anastasiia Soldatenkova
Publisher(s):
Elsevier BV; Elsevier Ltd
Tags:
Mathematics; Computer Science; Decision Sciences; Social Sciences; Bibliometrics; Productivity; Research evaluation; Universities; Statistics and Probability; Modeling and Simulation; Computer Science Applications1707 Computer Vision and Pattern Recognition; Management Science and Operations Research; Applied Mathematics; Settore ING-IND/35 - Ingegneria Economico-Gestionale; Italy; University; FSS; Funnel plot; Bibliometrics; CSS; FSS; Italy; Research evaluation; Statistics and Probability; Modeling and Simulation; Computer Science Applications1707 Computer Vision and Pattern Recognition; Management Science and Operations Research; Applied Mathematics; Bibliometrics; Productivity; Research evaluation; Universities; Statistics and Probability; Modeling and Simulation; Computer Science Applications1707 Computer Vision and Pattern Recognition; Management Science and Operations Research; Applied Mathematics; Statistics and Probability; Modeling and Simulation; Computer Science Applications1707 Computer Vision and Pattern Recognition; Management Science and Operations Research; Applied Mathematics; Alphabetical discrimination; Bibliometrics; Byline; Research evaluation; Statistics and Probability; Modeling and Simulation; Computer Science Applications1707 Computer Vision and Pattern Recognition; Management Science and Operations Research; Applied Mathematics; Bibliometrics; FSS; Funnel plot; Italy; Productivity; Universities; Statistics and Probability; Modeling and Simulation; Computer Science Applications1707 Computer Vision and Pattern Recognition; Management Science and Operations Research; Applied Mathematics; Bibliometrics; Italy; Research evaluation; University
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article description
The paper provides an empirical examination of how research productivity distributions differ across scientific fields and disciplines. Productivity is measured using the FSS indicator, which embeds both quantity and impact of output. The population studied consists of over 31,000 scientists in 180 fields (10 aggregate disciplines) of a national research system. The Characteristic Scores and Scale technique is used to investigate the distribution patterns for the different fields and disciplines. Research productivity distributions are found to be asymmetrical at the field level, although the degree of skewness varies substantially among the fields within the aggregate disciplines. We also examine whether the field productivity distributions show a fractal nature, which reveals an exception more than a rule. Differently, for the disciplines, the partitions of the distributions show skewed patterns that are highly similar.