Gender-related violence against women and its lethal outcome, feminicide, are a serious problem i... more Gender-related violence against women and its lethal outcome, feminicide, are a serious problem in Latin America and the Caribbean (LAC), as they are in the rest of the world. Although governments have passed legislation criminalizing feminicide, these laws have not been accompanied by relevant policy nor by robust data collection that measures the scope and scale of the problem. Drawing from Data Feminism, we situate feminicide data as "missing data" and describe the work of activists and civil society organizations who attempt to fill in the gaps by compiling incidents of feminicide from news reports. Activists doing this work face challenges: lack of time and financial resources, difficulties in accessing official data, and the mental health burden of reading about violent deaths of women. In this article, we describe our work-in-progress on a participatory action research project designed to help sustain activist efforts to collect feminicide data by partially automating detection using machine learning. We created and labeled a data set for identifying feminicide from media reports and trained a model using this data. The accuracy of the model on our test data set was 81.1%, which shows promise for reducing the labor required to identify and log feminicides, among Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).
Gender-related violence against women and its lethal outcome, feminicide, are a serious problem i... more Gender-related violence against women and its lethal outcome, feminicide, are a serious problem in Latin America and the Caribbean (LAC), as they are in the rest of the world. Although governments have passed legislation criminalizing feminicide, these laws have not been accompanied by relevant policy nor by robust data collection that measures the scope and scale of the problem. Drawing from Data Feminism, we situate feminicide data as "missing data" and describe the work of activists and civil society organizations who attempt to fill in the gaps by compiling incidents of feminicide from news reports. Activists doing this work face challenges: lack of time and financial resources, difficulties in accessing official data, and the mental health burden of reading about violent deaths of women. In this article, we describe our work-in-progress on a participatory action research project designed to help sustain activist efforts to collect feminicide data by partially automating detection using machine learning. We created and labeled a data set for identifying feminicide from media reports and trained a model using this data. The accuracy of the model on our test data set was 81.1%, which shows promise for reducing the labor required to identify and log feminicides, among Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).
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