Please note that this is an online event.
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This is to cordially invite you to the doctoral defense of
(Doctor of Philosophy in Network Science)
Computational and Relational Understanding of Gender Inequalities in Science and Technology
János Kertész (DNDS, chairperson, voting member)
Balázs Vedres (DNDS, supervisor, non-voting member)
Ildikó Barna (Faculty of Social Sciences, Eötvös Loránd University, reviewer, voting member)
David Garcia (Complexity Science Hub, Vienna / Medical University of Vienna, reviewer, voting member)
Date and time: Monday, June 22, 2020, 4:00 pm CEST
Venue: Online, via Bluejeans
ABSTRACT / Women are still a minority in Science and Technology, and gender discrimination persists, even though recent research suggests gender diversity can be beneficial in teamwork: female members increase the overall intelligence of teams, gender-diverse scientific teams are more creative and produce higher quality science, and diversity enforces objectivity, helps to process information more carefully and can reduce unconscious bias. However, in male-dominated fields, gender diversity has been associated with worse performance and lower success. Most diversity advocates agree that diversity without inclusive work practices will not help teams to perform better. As our lives rely heavily on scientific and technological innovation, the lack of diversity has high societal costs: unintended consequences of non-diverse scientific teams range from not developing proper medical interventions for women to not ensuring that technological innovations profit women and men equally.
Since success is a collective measure that captures a community’s reaction on one’s performance, (unconscious) gender bias can impact one’s reputation. For women, successful role models are crucial to envision a potential career in STEM, therefore identifying the micro-, meso- and macro-level behavior patterns that hold women back is crucial for better female representation. This work presents findings on how gendered behavior and gendered network formation influence women’s success in three male-dominated STEM fields which serve as gatekeepers for future STEM careers: Open Source Software Development, Academia and the Video Game Industry.
The purpose of this research is to use computational methods on large-scale data to explore how gender inequalities are embedded into social networks. This dissertation has three major contributions. First, the main contribution is applying data and network science methods on large datasets to uncover the relational complexity of hidden gender inequalities. The second important contribution is moving beyond the typical gender inequality research, which conceptualizes gender-based discrimination as categorical discrimination with quantifying gendered behavior based on users’ online activity. Third, a key contribution is introducing a new approach with relevant findings to the ongoing debate on positive and negative effects of team diversity.
Findings suggest that gendered behavior and gendered network formation are key drivers of online inequality, although the negative consequences of categorical gender stereotypes might still be present as well. Since the segregation of women is the product of a masculine culture in STEM fields, I argue that we cannot overcome gender inequality as long as a cultural shift does not happen.