南非地方政府数字公民参与中的权力参与--以MOBISAM为例
Power Participation in Digital Citizen Engagement in South African Local Government: The Case of MOBISAM
摘要：
缺乏服务提供和问责制是南非许多城市（地方政府）面临的两个典型挑战。数字化公民参与机制是当地大学、民间社会、居民和当地政府之间的一项合作努力，旨在促进数字化公民参与，促进当地政府与公民之间的双向沟通，并为改善基本服务的提供做出贡献。通过该项目的实施过程，人们清楚地看到，在知识方面，权力在地方政府与公民之间的互动或缺乏互动中发挥作用。这篇正在进行中的论文开始了探索在ESAM项目中发挥作用的权力/知识动态的旅程（使用SECI模型和Power Cube），方法是拆开并理解项目中处理的知识的性质以及随着时间的推移在不同项目利益相关者之间发生的相关知识创造过程。权力的影响，在确定项目的关键利益相关者之间的有效知识转移，能力建设和组织学习已经成为一个重要的问题，需要彻底的调查和批判性的分析。这篇正在进行的论文介绍了与知识和权力的相互作用相关的阶段的研究结果的初步框架，这些阶段是：1）实现，2）导航响应，3）涌现。
英文摘要：
A lack of service delivery and accountability are two characteristic challenges of numerous municipalities (local government) in South Africa. MobiSAM was introduced as a collaborative effort between a local university, civil society, residents, and local government to grow digital citizen engagement, facilitate two-way communication between local government and its citizenry, and contribute to the improved provision of basic services. Through the course of the project, it became clear that power, with respect to knowledge, was at play in the interactions or lack thereof between local government and citizens. This work-in-progress paper begins the journey of exploration of the power/knowledge dynamics at play in the MobiSAM project (using the SECI model and the Power Cube) by unpacking and understanding the nature of knowledge processed in the project and the associated knowledge creation processes that ensued between the different project stakeholders over time. The influence of power in determining the effective transfer of knowledge between key stakeholders of the project, for capacity building and organisational learning has emerged as an important issue in need of thorough investigation and critical analysis. This work in progress paper presents the preliminary framing of the research findings on the phases associated with the interplay of knowledge and power in the MobiSAM project, which are: 1) Realisation, 2) Navigating Responsiveness, and 3) Emergence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
