RT Journal Article
SR Electronic
T1 A data-driven approach to reduce gender disparity in invited speaker programs at scientific meetings
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 426320
DO 10.1101/426320
A1 Ann-Maree Vallence
A1 Mark R Hinder
A1 Hakuei Fujiyama
YR 2018
UL http://biorxiv.org/content/early/2018/12/21/426320.abstract
AB Gender disparity continues to be an issue in STEM, with progress requiring consistent and focused efforts. Here, we present a data-driven approach to promote high quality, gender balanced invited speaker selection for neuroscience conferences. We have targeted invited speaker opportunities because underrepresentation of female speakers at international neuroscience conferences remains a major problem, and such opportunities are critical for career development. First, we audited the top ten neuroscience journals (indexed by SCImago Journal and Country Rank; SJR), identifying (1) highly cited papers, (2) gender of first and last authors, and (3) field-weighted citation impact and total publications of first and last authors. Second, we used these data to establish a database of high quality scientists that could be used to select speakers for conferences. We found that research quality (as indexed by field-weighted citation impact and total publications) of authors of highly cited publications in the top 10 neuroscience journals did not differ significantly for females and males. The comparison between the gender base rate in neuroscience and authors publishing highly cited papers in high-quality neuroscience journals shows that female representation, particularly at last author level, is less than the estimated base rate for neuroscience. In summary, we present a data-driven approach to invited speaker selection that would facilitate gender balanced conference programs while maintaining the highest of scientific standards. This approach minimizes the influence of implicit gender bias in speaker selection decisions by using scientific quality metrics that STEM researchers are familiar with, and indeed use to evaluate their own performance. Having an immediate effect on reducing gender disparity in conference programs, our approach would generate a positive spiral for more long-term reduction of gender disparity in STEM.Significance Statement Gender disparity is a persistent issue in STEM. We present a data-driven approach to invited speaker selection, based on scientific quality metrics that researchers use to evaluate their own and their peers’ performance. We targeted invited speaker opportunities because underrepresentation of female speakers at international conferences remains a major problem, and such opportunities are critical for career development. Research quality of authors of highly cited publications in top neuroscience journals did not differ between females and males. This approach minimizes implicit gender bias in speaker selection, which will immediately reduce gender disparity in conference programs, as well as generate a positive spiral for more long-term reduction of gender disparity in STEM.