未来回避的日常:管理数据驱动的智慧城市

IF 0.6 4区 管理学 Q1 HISTORY
Leah Horgan
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引用次数: 4

摘要

摘要:根据对洛杉矶数据驱动治理的为期两年的民族志研究,这项研究表明,尽管人们在很大程度上使用智能、数据驱动的方法来创造更好、更可持续、更互联的城市未来,但数据驱动治理的日常实践却围绕着防止不必要的未来而展开。换言之,虽然智慧城市的言论承诺了一个透明、高效和幸福的乌托邦,但智慧城市工具的实际应用却贯穿于它们的对立:防止浪费、犯罪、灾难等等。本研究详细介绍了两个旨在通过预测来预防的行政项目——预防犯罪和防止无家可归——,预防逻辑和预测分析的耦合作用是什么?我建议,通过避免不想要的未来来提供更好的未来,可以扩大智能城市的认知基础设施,并由此扩大对监控的依赖。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Everyday of Future-Avoiding: Administering the Data-Driven Smart City
abstract:Drawing on a two-year ethnographic study of data-driven governance in Los Angeles, this study shows that while much is made of using smart, data-driven approaches to make better, more sustainable, and more connected city futures, the everyday practices of data-driven governance are instead wrapped around efforts to prevent unwanted futures. Put another way, while the rhetoric of the smart city promises a utopia of transparency, efficiency, and well-being, the practical application of smart city tools is cast through their opposites: preventing waste, crime, disaster, and so on. Detailing two administrative projects that aim to prevent through prediction—crime prevention and homelessness prevention—this study asks, What does the coupling of prevention logics and predictive analytics do? I suggest that rendering preferable futures by avoiding unwanted ones expands the epistemic infrastructure of the smart city and, with it, reliance on surveillance.
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来源期刊
CiteScore
0.80
自引率
16.70%
发文量
18
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