Yessica Macià Martínez, Laura Fernández, Núria Ferran-Ferrer
2025.12.12Data Technologies and Applications
Abstract
This study explores the pervasive gender bias in the editorial decision-making processes of several Wikipedia language editions (Catalan, Italian and French). It examines how these processes – focused on deliberations and voting – affect the representation of gender diversity. Specifically, it evaluates the implications of banning gender-based categories for accessing information, rejecting inclusive language and the controversial practice of including trans individuals' dead names in biographies. The research employs a detailed content analysis of arguments presented during nine deliberative and voting processes conducted between 2014 and 2024. A redesigned codebook was utilised to classify and analyse arguments based on their type and stance toward gender inclusion. The study also coded editors' gender identities, where identifiable, to assess patterns of participation and perspectives within these processes. The findings reveal a systemic resistance to gender diversity in decision-making processes, predominantly justified through appeals to values, neutrality policies and community norms. While arguments supporting inclusivity often emphasised ethical considerations, practical reasoning and precedence, they were insufficient to sway outcomes in favour of gender inclusion. The lack of consensus and resistance perpetuates the invisibility of gender-diverse identities, reinforcing existing inequalities. This research uniquely examines Wikipedia's decision-making infrastructure and its broader implications for knowledge representation. By highlighting the systemic barriers to inclusivity in collaborative knowledge platforms, it contributes to critical discussions on diversity and equity in digital information systems.
Citation format
MARTÍNEZ, Yessica Macià; FERNÁNDEZ, Laura; FERRAN-FERRER, Núria. Decision-making on wikipedia: An analysis of gender bias and its impact on discoverability and information retrieval. Data Technologies and Applications, 2025.