JOURNAL ARTICLE
Symbolic and Transformative: Alignments Toward Feminicídio Legal Reform inside the Brazilian Police.
Published In: Social Politics: International Studies in Gender, State & Society, 2024, v. 31, n. 1. P. 99 1 of 3
Database: Sociology Source Ultimate 2 of 3
Authored By: Pamplona, Roberta S 3 of 3
Abstract
This article examines how law enforcement actors in Porto Alegre, Brazil, adopt the Feminicídio Law—a feminist-inspired legal reform that increases penalties for gender-based homicides. Drawing on one year of fieldwork in homicide and women's police stations, police inquiry records, and local media, the study finds that while all officers recognize and use the feminicídio legal category, their alignments differ: homicide division officers symbolically accept the reform without changing investigative practices, whereas women's police station officers adopt a transformative alignment, advocating for new training, collaboration with feminist movements, and institutional changes. These divergent responses reflect institutional roles, professional beliefs, and prior feminist engagements within the police, illustrating the uneven incorporation of feminist ideas in state structures amid Brazil's contested political context. The findings highlight the importance of institutional context and prior feminist interventions in shaping how feminist legal reforms are interpreted and implemented by state actors.
Additional Information
- Source:Social Politics: International Studies in Gender, State & Society. 2024/03, Vol. 31, Issue 1, p99
- Document Type:Article
- Subject Area:Religion and Philosophy
- Publication Date:2024
- ISSN:1072-4745
- DOI:10.1093/sp/jxad019
- Accession Number:176103818
- Copyright Statement:Copyright of Social Politics: International Studies in Gender, State & Society is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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