An investigation on the skewness patterns and fractal nature of research productivity distributions at field and discipline level. Issue 1 (February 2017)

• Record Type: Journal Article
• Authors: Abramo, Giovanni; D'Angelo, Ciriaco Andrea; Soldatenkova, Anastasiia
• Is Part Of: Journal of informetrics. Volume 11:Issue 1(2017:Feb.)
• Page Start: 324
• Page End: 335
• Publication Date: 2017-02
• Subjects: Bibliometrics -- Research evaluation -- FSS -- Italy -- CSS; 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.2017.02.001
• Languages: English
• ISSNs: 1751-1577

Abstract

Highlights: Research productivity distributions are investigated by the CSS technique. The analysis is carried out at field (180) and discipline (10) level. Skewness varies substantially among the fields within the aggregate disciplines. At field level the fractal nature of the distributions occurs in very few cases. Abstract: 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.