社会数据治理:走向定义和模型

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
Jun Liu
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引用次数: 9

摘要

随着全球数据和数据化治理举措、安排和实践的数量激增,了解各种类型的此类举措、安排及其结构性原因已成为学者、决策者和公众的艰巨任务。这种复杂性另外在考虑彼此可公度的不同数据(化)治理时产生了实质性的困难。为了推进讨论,本研究认为,现有的学术倾向于采用以组织为中心的观点,主要关注组织层面的数据和数据化的因素和动态,而牺牲了数据和治理的宏观层面的社会、政治和文化因素。为了解释数据治理的宏观、社会维度,本研究提出了“社会数据治理”一词,以提出这样一种观点,即数据治理不仅反映了它产生的社会,而且(重新)产生了相关社会的政策和实践。借鉴政治学和公共管理理论,提出了一个社会数据治理模型,从比较的角度阐明了各种治理模式的思想和概念基础。这一初步模型由二维连续体、国家干预和社会自治以及国家文化组成,解释了不同社会的社会数据治理变化,作为超越欧洲观点对数据治理进行概念化和分类的补充方式。最后,我们对疫情期间的数字接触者追踪技术进行了极端案例研究,以证明所提出的社会数据治理模型的解释力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social data governance: Towards a definition and model
With the surge in the number of data and datafied governance initiatives, arrangements, and practices across the globe, understanding various types of such initiatives, arrangements, and their structural causes has become a daunting task for scholars, policy makers, and the public. This complexity additionally generates substantial difficulties in considering different data(fied) governances commensurable with each other. To advance the discussion, this study argues that existing scholarship is inclined to embrace an organization-centric perspective that primarily concerns factors and dynamics regarding data and datafication at the organizational level at the expense of macro-level social, political, and cultural factors of both data and governance. To explicate the macro, societal dimension of data governance, this study then suggests the term “social data governance” to bring forth the consideration that data governance not only reflects the society from which it emerges but also (re)produces the policies and practices of the society in question. Drawing on theories of political science and public management, a model of social data governance is proposed to elucidate the ideological and conceptual groundings of various modes of governance from a comparative perspective. This preliminary model, consisting of a two-dimensional continuum, state intervention and societal autonomy for the one, and national cultures for the other, accounts for variations in social data governance across societies as a complementary way of conceptualizing and categorizing data governance beyond the European standpoint. Finally, we conduct an extreme case study of governing digital contact-tracing techniques during the pandemic to exemplify the explanatory power of the proposed model of social data governance.
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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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