Feminicide & machine learning : detecting gender-based violence to strengthen civil sector activism

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IDL-60535.pdf (640.92 KB)

Date

2020-08

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Abstract

Although governments have passed legislation criminalizing feminicide, it is unaccompanied by relevant policy or robust data collection. This participatory action research project is designed to help sustain activist efforts to collect feminicide data through partially automated detection using machine learning. As a way to counter the impunity surrounding feminicide, activists have taken upon themselves to do the work that states have neglected. Partially automating detection supports efforts to systematize and sort data collection across contexts, and helps to inform policy advocacy through standardizing definitions and taxonomies. The ability to prioritize articles by likelihood of feminicide will make this intense research less gruelling.

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DATA COLLECTION, MACHINE LEARNING, FEMICIDE, FEMINICIDE, ARTIFICIAL INTELLIGENCE, INTEROPERABILITY, TAXONOMY, PARTICIPATORY ACTION RESEARCH, ACTIVISM, RAPE, MURDER, DOMESTIC ABUSE, RESEARCH CAPACITY BUILDING, ACCESS TO INFORMATION, VIOLENCE AGAINST WOMEN, GLOBAL

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http://hdl.handle.net/10625/60535

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IDRC Research Results / Résultats de recherches du CRDI
2020-2029 / Années 2020-2029
Research Results (OD4D) / Résultats de recherche (DOD)