情境化现实主义:数字孪生数据化中的观看和记录行为分析

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY Big Data & Society Pub Date : 2023-01-01 DOI:10.1177/20539517231155061
Paulan Korenhof, E. Giesbers, Janita Sanderse
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引用次数: 1

摘要

数字双胞胎被概念化为现实生活中物理实体或系统的实时数字表示。它们被探索用于广泛的社会实施,特别是帮助解决根本的社会挑战。作为现实生活中双胞胎的精确数字等价物,数字双胞胎在知识生产和决策过程中取代了物理双胞胎。他们提出了很高的期望:他们被期望产生新的知识,尽早发现问题,预测未来的行为,并帮助优化身体双胞胎。数据在这里起着关键作用,因为它们构成了创建数字孪生表示的构建块。然而,数据不是中性现象,而是人类技术互动的产物。因此,在本文中,我们提出了一个问题,即如何创建数字双胞胎数据集,以及这对数字双胞胎有什么影响?为了回答这个问题,我们在一所大学的三个数字孪生开发案例中探索了数据收集过程。结合Jasanoff的视觉制度理论框架,我们将数据收集的创建视为观察和记录行为,这些行为会影响现实在数据中的表现方式,并赋予数据收集一定的合法性和权威性。通过研究观看和记录的行为及其在产生数据收集中的各自作用,我们深入了解了数字双胞胎中的表现斗争及其影响。
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Contextualizing realism: An analysis of acts of seeing and recording in Digital Twin datafication
Digital Twins are conceptualized as real-time digital representations of real-life physical entities or systems. They are explored for a wide array of societal implementations, and in particular to help address fundamental societal challenges. As accurate digital equivalents of their real-life twin, Digital Twins substitute their physical twin in knowledge production and decision-making processes. They raise high expectations: they are expected to produce new knowledge, expose issues early, predict future behavior, and help to optimize the physical twin. Data play a key role here because they form the building blocks from which the Digital Twin representation is created. However, data are not neutral phenomena but products of human-technology interaction. In this article, we therefore raise the question of how a Digital Twin data collection is created, and what implications does this have for Digital Twins? To answer this question, we explore the data collection process in three cases of Digital Twin development at a university. Connecting to Jasanoff's theoretical framework of regimes of sight, we approach the creation of a data collection as acts of seeing and recording that influence how reality is represented in data, as well as give a certain legitimacy and authority to the data collection. By examining the acts of seeing and recording and their respective roles in producing the data collection, we provide insight into the struggles of representation in Digital Twins and their implications.
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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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