Materiality of Memorialisation: Mapping Migrant Women's Landmarks in Europe

This article investigates the memorialization of migrant women across transcultural landscapes, and analyses results from the Register of Migrant Women Landmarks in Europe (hereinafter RMWLE), central to the European Cooperation in Science and Technology (COST) action project "Women on the Move" (CA19112 - WEMov). It serves as reference for subsequent research based on data from this Register, for which data collection is continuing. The RMWLE registers landmarks, such as monuments, plaques, streets and other toponymic infrastructures named after women with a significant history of migration. It honours aspects rarely prioritized in memorialization agendas, which are skewed towards men's stories, and towards the more linear biographies of sedentary figures whose European, national, and regional memorialization have remained uncomplicated by migration. This Deep Data study reveals recurring patterns at the level of Europe in the memorialization of these women migrants. The diversity of stories, the richness and the prominence of landmarks devoted to men compared to women is a subject well-covered in memorialization studies. This unbalance is compounded by the data from our register which shows landmarks on women migrants that are sometime tokenized, often marginalized, and which reproduce the bias towards nurture and care that have besieged the memorialization of women in general. It further shows that the memorialization process and the political and cultural mechanisms of official commemoration often work against the recognition of cross-border careers and stories. The intersectionality of the project, highlighting both gender and migration, uncovers a political landscape of landmarks - and we reflect on how this register can help combat cultural prejudice by recovering migration episodes. The RMWLE helps us reflect on the defining impact of migration episodes, a reality rarely underlined in the biographies of famous women. This article is based on a quantitative content analysis, focused on identifying measurable features and frequencies within the RMLWE dataset, and combines it with a storytelling approach at the interpretation stage, to counter dominant cultural narratives and knowledge practices.