The mining of China's policies against COVID-19 based on a policy target and tool co-evolution framework

Abstract

Mining all COVID-19 policies issued by China can provide valuable lessons for both China and other countries in future pandemic control efforts. In this paper, we introduce a novel framework for mining the co-evolution of policy targets and policy tools. We employ bibliometric methods, text mining, and network analysis to explore the entire evolution of China's COVID-19 policies. Examining 1154 central government policies, (a) we extract policy targets from each policy, uncovering their evolution across different stages of the pandemic; (b) propose to identify the policy tool used in each policy unit by integrating an automatic identification model and active learning. We also reveal the categorical structure of these tools; (c) characterize the co-evolution pattern between policy targets and policy tools, shedding light on their dynamic relationship. Our findings indicate that policy targets have shifted across various stages, revealing unique characteristics in China's COVID-19 prevention and control efforts. Notably, there is a self-paradox between prevention measures and economic development. We identify the inadequateness in the distribution and utilization of policy tools. Ensuring the alignment of policy targets with appropriate tools is crucial. This synchronization and co-evolution between policy targets and tools are essential for enhancing the functional approach to policy implementation. This paper is the first systematic mining and review about the COVID-19 policies issued by the Chinese government, and our policy target and tool co-evolution mining framework provides a new quantitative framework for policy mining, especially an improved large language model and active learning theory are integrated to identify the policy tools automatically.