既不不透明也不透明:调查欧盟边境数据化的跨学科方法

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
Ana Valdivia, Claudia Aradau, Tobias Blanke, S. Perret
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引用次数: 7

摘要

2020年,欧盟宣布将边境管制新数据库出入境系统的生物识别部分合同授予两家公司:IDEMIA和Sopra Steria。这两家公司此前都参与了边境和移民管理数据库的开发。尽管越来越多的公开文件显示了正在实施什么样的技术、投入多少资金以及由谁来实施,但在该领域对数字方法的参与有限。此外,关键的边境和安全学术主要侧重于定性和人种学方法。在数据女权主义方法的基础上,我们提出了一种跨学科的方法,超越了定性/定量和不透明/透明的二元体系,研究了权力不对称,并使编码的劳动变得可见。根据经验,我们建立并分析了两个欧盟机构授予的合同数据集,这两个机构对其边境管理政策至关重要——欧洲大规模信息系统署(eu LISA)和欧洲边境和海岸警卫队(Frontex)。我们通过仔细阅读招标文件来补充公司网络的数字分析和可视化。通过这样做,我们展示了跨学科方法如何成为在欧盟边境“可理解”数据化的一种手段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neither opaque nor transparent: A transdisciplinary methodology to investigate datafication at the EU borders
In 2020, the European Union announced the award of the contract for the biometric part of the new database for border control, the Entry Exit System, to two companies: IDEMIA and Sopra Steria. Both companies had been previously involved in the development of databases for border and migration management. While there has been a growing amount of publicly available documents that show what kind of technologies are being implemented, for how much money, and by whom, there has been limited engagement with digital methods in this field. Moreover, critical border and security scholarship has largely focused on qualitative and ethnographic methods. Building on a data feminist approach, we propose a transdisciplinary methodology that goes beyond binaries of qualitative/quantitative and opacity/transparency, examines power asymmetries and makes the labour of coding visible. Empirically, we build and analyse a dataset of the contracts awarded by two European Union agencies key to its border management policies – the European Agency for Large-Scale Information Systems (eu-LISA) and the European Border and Coast Guard Agency (Frontex). We supplement the digital analysis and visualisation of networks of companies with close reading of tender documents. In so doing, we show how a transdisciplinary methodology can be a device for making datafication ‘intelligible’ at the European Union borders.
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来源期刊
Big Data & Society
Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
10.90
自引率
10.60%
发文量
59
审稿时长
11 weeks
期刊介绍: Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.
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