Assessment of gender divide in scientific communities

Antonio De Nicola () and Gregorio D’Agostino ()
Additional contact information
Antonio De Nicola: Casaccia Research Centre
Gregorio D’Agostino: Casaccia Research Centre

Scientometrics, 2021, vol. 126, issue 5, No 5, 3807-3840

Abstract: Abstract Increasing evidence of women’s under-representation in some scientific disciplines is prompting researchers to expand our understanding of this social phenomenon. Moreover, any countermeasures proposed to eliminate this under-representation should be tailored to the actual reasons for this different participation. Here, we take a multi-dimensional approach to assessing gender differences in science by representing scientific communities as social networks, and using data analytics, complexity science methods, and semantic methods to measure gender differences in the context, the attitude and the success of scientists. We apply this approach to four scientific communities in the two fields of computer science and information systems using the network of authors at four different conferences. For each discipline, one conference is based in Italy and attracts mostly Italians, while one conference is international in both location and participants. The present paper provides evidence against common narratives that women’s under-representation is due to women’s limited skills and/or less social centrality.

Keywords: Gender; Social networks; Semantics; Complex networks (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-021-03885-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:126:y:2021:i:5:d:10.1007_s11192-021-03885-3

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-021-03885-3

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().