“众创”试验:理解中国政策创新的新视角——基于干部容错纠错机制演化的案例研究

“Crowd Innovation”Experiment:A New Perspective to Understand China's Policy Innovation:A Case Study of Fault Tolerance and Error Correction Mechanism for Cadres

试验作为一种具有鲜明实用主义特征的政策创新策略,能够结合实践需要表现为不同样态。当前,关于政策试验的研究大都默认以“试点-推广”框架作为前提和基础,而忽视了试验的多样性特征。研究基于对国家治理实践的观察和对既有框架解释局限性的反思,在经典的“试点-推广”模式之外,提出了一种新的竞争性解释框架,即“众创”试验。这种试验模式主要用于应对那些具有复杂程度高、时间压力大、地方情境差异明显等特征的政策创新议题,并且遵循一条截然不同的试验路径。论文以干部容错纠错机制的形成与演化过程为例,借助实地调研资料和相关公开数据,剖析了“众创”试验的实际过程。研究发现,“众创”试验下的政策创新过程具有开放式探索、累进式创新、差异化选择的特征,并且在目标取向、中央角色、知识生产、创新扩散、发展路径方面呈现出与传统“试点-推广”试验不同的深层逻辑。“众创”试验框架为理解我国政策创新过程提供了一个新视角,有助于推动政策试验领域的知识增长。

Experiment is a policy innovation strategy with distinct pragmatic characteristics,which can be expressed in different forms according to practical needs.Most of the existing studies on policy experiments take the“pilot-promotion”model as the premise and basis,without paying attention to the diversity of policy experiments.Based on the observation of governance practices and reflections on the limitations of existing analytical frameworks,this study proposes a new competitive interpretation framework,namely,the“Crowd Innovation”experiment.This analytical framework is used to examine policy innovation issues characterized by high complexity,time constraints,and obvious differences in local contexts.Using the formation and evolution of fault tolerance and error correction mechanism as an example,this paper analyzes the process of the“Crowd Innovation”experiment with the help of field research data and public data.The policy innovation process in the“Crowd Innovation”experiment mode was characterized by open exploration,progressive innovation,and differentiated selection.Moreover,it presents a deep logic different from the traditional“pilot-promotion”analytical framework in terms of target orientation,role of the central government,knowledge production,policy diffusion,and development path.The“Crowd Innovation”experiment model provides a new perspective for understanding the policy innovation process in China,and helps to promote the growth of knowledge in the field of policy experiment.