EconPapers    
Economics at your fingertips  
 

Digital technology helps remove gender bias in academia

Julie Fortin, Bjarne Bartlett, Michael Kantar (), Michelle Tseng and Zia Mehrabi ()
Additional contact information
Julie Fortin: University of British Columbia
Bjarne Bartlett: University of Hawai’i
Michael Kantar: University of Hawai’i
Michelle Tseng: University of British Columbia
Zia Mehrabi: University of British Columbia

Scientometrics, 2021, vol. 126, issue 5, No 17, 4073-4081

Abstract: Abstract Science attempts to be a meritocracy; however, in recent years, there has been increasing evidence for systematic gender bias against women. This bias is present in many metrics commonly used to evaluate scientific productivity, which in turn influences hiring and career success. Here we explore a new metric, the Altmetric Attention Score, and find no evidence of bias across many major journals (Nature, PNAS, PLOS One, New England Journal of Medicine, Cell, and BioRxiv), with equal attention afforded to articles authored by men and women alike. The exception to this rule is the journal Science, which has marked gender bias against women in 2018, equivalent to a mean of 88 more tweets or 11 more news articles and a median of 20 more tweets or 3 more news articles for male than female first authors. Our findings qualify Altmetric, for many types and disciplines of journals, as a potentially unbiased measure of science communication in academia and suggest that new technologies, such as those on which Altmetric is based, might help to democratize academic evaluation.

Keywords: Altmetric; Gender bias; Academia; Science communication (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-021-03911-4 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_s11192-021-03911-4

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

DOI: 10.1007/s11192-021-03911-4

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 ().

 
Page updated 2025-03-20
Handle: RePEc:spr:scient:v:126:y:2021:i:5:d:10.1007_s11192-021-03911-4