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| 论文编号: | 15557 | |
| 作者编号: | 2320234197 | |
| 上传时间: | 2025/12/5 16:11:07 | |
| 中文题目: | 基层政府数据治理问题及对策研究 | |
| 英文题目: | Research on Data Governance Issues and Countermeasures in Grassroots Governments | |
| 指导老师: | 王芳 | |
| 中文关键字: | 政府数据治理;基层负担;技术应用 | |
| 英文关键字: | Government data governance; Grassroots burden; Technology application | |
| 中文摘要: | 在数字时代,数据已成为关键生产要素,但现有政府数据治理研究多集中于宏观层面,对县级这一连接国家战略与基层实践的关键节点存在“层级断层”。本研究试图对这一问题进行初步探索。通过对中国西部具有代表性的D县进行实证研究,系统剖析当前基层政府在数据治理实践中面临的深层困境与挑战,并探索优化路径。 本文构建了以信息生态理论为背景,整合整体性治理理论、街头官僚理论和行政负担理论为核心的“1+3”分析框架,分别用于剖析数据治理中的“碎片化”协同难题、基层执行者的“自由裁量权”行为逻辑,以及数据在流转与应用中产生的多重成本。本文采用解释性顺序混合研究设计,包括问卷调查和深度访谈。首先运用网络调查方式系统梳理可以查证的政策文本以及平台建设情况,并通过问卷调查形式,对D县政府数据治理现状进行量化评估。随后基于定量发现开展针对性的深度访谈,深入挖掘D县政府数据治理现存问题。研究发现D县政府数据治理虽然取得初步成效,但仍面临三大严峻挑战:制度“碎片化”导致治理“分散化”、基层负担过重导致政策执行变形、平台建设不力导致价值赋能不足。 为破解上述难题,本研究提出一套覆盖制度、技术、人三个层面系统性对策。在制度层面,推行实体化政府数据治理统筹领导及配套制度,并优化考核导向,将考核重心从“重时效”向“重质量、促共享、减负担”转变;在技术层面,深化技术应用,综合利用物联网、光学字符识别及人工智能等手段赋能基层减负,并优化平台建设,搭建内部数据中台与优化平台设计,解决资源“空心化”问题;在人员素养层面,面向干部加强业务培训,构建分级分类的素养提升体系,面向群众加强宣传引导,普及安全知识并展示治理成效,从而营造良好的数据治理生态。 | |
| 英文摘要: | In the digital age, data has become a key factor of production, but the existing government data governance research is mostly focused on the macro level, and there is a "hierarchical fault" at the county level, a key node connecting national strategy and grassroots practice. This study aims to fill this gap by conducting an empirical study of D County, a representative county in western China, to systematically analyze the deep dilemmas and challenges faced by grassroots governments in data governance practice, and explore optimization paths. This paper constructs a '1+3' analytical framework based on the information ecology theory, integrating holistic governance theory, street-level bureaucracy theory, and administrative burden theory. It is respectively used to analyze the challenges of 'fragmented' coordination in data governance, the behavioral logic of grassroots implementers' 'discretionary power,' and the multiple costs arising from data circulation and application. This paper adopts an explanatory sequential mixed research design, including questionnaire surveys and in-depth interviews. First, a web-based survey is used to systematically review verifiable policy documents and platform construction, and through a questionnaire survey, the current situation of data governance in County D’s government is quantitatively assessed. Subsequently, targeted in-depth interviews are conducted based on quantitative findings to explore existing problems in County D’s government data governance. The study finds that although the data governance of County D’s government has achieved preliminary results, it still faces three major challenges: institutional 'fragmentation' leading to governance 'decentralization,' excessive burdens on grassroots staff causing policy implementation distortion, and weak platform construction resulting in insufficient value empowerment. To address the aforementioned challenges, this study proposes a set of systematic countermeasures covering the institutional, technological, and human dimensions. At the institutional level, it advocates the implementation of a structured government data governance framework with coordinated leadership and supporting regulations, while optimizing evaluation guidance by shifting the focus of assessments from "timeliness" to "quality, promoting sharing, and reducing burdens." At the technological level, it promotes the deepening of technology applications, leveraging tools such as the Internet of Things, optical character recognition, and artificial intelligence to reduce workloads at the grassroots level, and optimizing platform construction by establishing an internal data hub and improving platform design to address the problem of resource "hollowing out." At the level of personnel competence, it focuses on strengthening business training for officials by building a tiered and categorized skill enhancement system, and enhancing public awareness through education and guidance to disseminate safety knowledge and demonstrate governance effectiveness, thereby fostering a healthy data governance ecosystem. | |
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