实现公平且可付诸行动的新冠疫情数据的七条交叉性女权主义原则。
Seven intersectional feminist principles for equitable and actionable COVID-19 data.
作者信息
D'Ignazio Catherine, F Klein Lauren
机构信息
Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA.
Departments of English and Quantitative Theory and Methods, Emory University, Atlanta, GA, USA.
出版信息
Big Data Soc. 2020 Jul 30;7(2):2053951720942544. doi: 10.1177/2053951720942544. eCollection 2020 Jul.
This essay offers seven intersectional feminist principles for equitable and actionable COVID-19 data, drawing from the authors' prior work on data feminism. Our book, Data Feminism (D'Ignazio and Klein, 2020), offers seven principles which suggest possible points of entry for challenging and changing power imbalances in data science. In this essay, we offer seven sets of examples, one inspired by each of our principles, for both identifying existing power imbalances with respect to the impact of the novel coronavirus and its response, and for beginning the work of change.
本文借鉴作者先前关于数据女权主义的研究成果，提出了七条交叉性女权主义原则，以实现公平且可行的新冠疫情数据。我们的著作《数据女权主义》（迪尼亚齐奥和克莱因，2020年）提出了七条原则，这些原则为挑战和改变数据科学中的权力失衡提供了可能的切入点。在本文中，我们提供了七组示例，每组示例都受我们其中一条原则的启发而来，用于识别在新型冠状病毒的影响及其应对措施方面现存的权力失衡，并开启变革工作。
Zotero 插件