政策试验中的议题学习机制——以X市"多规合一"改革为例
Topic Learning Mechanism in Policy Experimentation:A Case Study of the"Multi-Plan Integration"Reform in X City
赵远跃1
作者信息
- 1. 中国人民大学公共管理学院,北京 100872
- 折叠
摘要
政策试验既是推动改革的重要策略,也是改进政策安排和提升治理适应性的有效手段.在高度模糊的试验情景下,决策者通过创设议题学习网络,鼓励地方官员、政策专家、利益相关者参与,在持续的互动中,促进了不同来源知识的分享与应用.在文献回顾基础上,本文提出政策试验的"议题学习-知识生产"分析框架,区分了议题学习的三种模式,即地方官员驱动的规范学习、政策专家驱动的认知学习和利益相关者驱动的社会学习,三种模式分别生产合规性知识、科学性知识和情境性知识.基于X市"多规合一"改革的实证分析,本文识别了议题学习的权威倡导机制、迭代协商机制、双轨驱动机制、议题再建构机制,阐释了其内在逻辑关系.促进政策试验中的议题学习需要区分不同学习模式,了解不同参与者的身份特征,有针对性设计政策措施,形成合作型知识生产模式.
Abstract
Policy experimentation is not only an important strategy to promote reform,but also an effective means to improve policy arrangement and enhance governance adaptability.In the highly ambiguous experiment scenario,decision-makers encourage the participation of local officials,policy experts,and stakeholders through the creation of a topic-learning network,which promotes the sharing and application of knowledge from different sources in the continuous interaction.Based on the literature review,this paper proposes an analytical framework of"issue learning-knowledge production"for policy experimentation and distinguishes three models of issue learning,namely normative learning driven by local offi-cials,cognitive learning driven by policy experts and social learning driven by stakeholders.The three models produce compliance knowledge,sci-entific knowledge,and situational knowledge respectively.Based on the empirical analysis of the"multi-plan integration"reform in X City,this pa-per identifies the scenario adjustment mechanism,dual-track learning mechanism,authoritative advocacy mechanism,iterative negotiation mecha-nism,and topic reconstruction mechanism of issue learning.To promote issue learning in policy experimentation,it is necessary to distinguish dif-ferent learning modes,understand the identity characteristics of different participants,design targeted policies and measures,and form a cooperative knowledge production mode.
关键词
政策试验/议题学习/知识生产/多规合一/多方互动Key words
Policy Experimentation/Issue Learning/Knowledge Production/Multi-Gauge Integration/Multi-Party Interaction引用本文复制引用
基金项目
中国人民大学科学研究基金(中央高校基本科研业务费专项资金资助)(23XNH036)
出版年
2024
NETL
NSTL
万方数据