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The Emerging Big Data Ecosystem and Digital Transformation of Poverty Governance in China.
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- Author(s): Zhongyuan, Wang; Woo, Su Yun; Kübler, Daniel
- Source:
Social Sciences in China; May2024, Vol. 45 Issue 2, p152-183, 32p- Subject Terms:
- Source:
- Additional Information
- Subject Terms:
- Abstract: Big data technology has become increasingly prevalent in facilitating the delivery of government services and public goods in China and beyond. This article seeks to examine how big data can be leveraged in poverty governance in rural China, and what the political implications are. This phenomenon of big data-driven welfare governance is particularly salient considering the broader context of governmental digital transformation, both within China and globally. This research sheds light on how big data usage functions in poverty alleviation, highlighting general motivations and developments in Chinese data-centric welfare governance. More importantly, this article introduces the novel concept of "digital cybernetic capacity" to examine public sector modernization as big data transforms the landscape of welfare delivery and governance. By delving into the intersection between technology and social welfare, we explicate how the use of big data in social welfare policies can go beyond merely improving information capacity to redefine state capacity. This article argues that such a new governance ecosystem, driven as it is by data technologies, not only provides insights about the transformative resilience of Chinese governance but also opens a new theoretical frontier for research into other countries. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Social Sciences in China is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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