政策工具识别与分类研究的范式演进:从碳基经验到硅基智能  

The Paradigm Evolution in the Identification and Classification of Policy Instruments:From Carbon-Based Experience to Silicon-Based Intelligence

作　　者：杜玉春 黄一然 DU Yuchun;HUANG Yiran

机构地区：[1]清华大学数字政府与治理研究院、社会科学学院 [2]清华大学数字政府与治理研究院

出　　处：《社会治理》2025年第2期73-84,共12页Social Governance Review

基　　金：国家社科基金青年项目“数字智能时代算法决策的创新机制与风险防范研究”(编号:23CZZ043);北京市哲学社会科学项目“政务大模型驱动接诉即办数字化建设研究”(编号:20245040021)阶段性成果。

摘　　要：政策工具识别与分类是政策科学研究中的基础问题之一。通过系统梳理技术方法演进脉络,将相关研究划分为四个发展阶段,以揭示技术驱动下的研究范式变迁。在1.0阶段,研究者凭借自身经验和专业知识,采用质性分析和规范分析方法展开研究。进入2.0阶段,内容分析法成为主流,计算机辅助工具的应用实现了结构化编码与描述统计,分析的系统性和可重复性显著提升。3.0阶段的突破体现在自然语言处理算法模型的引入,在保持语义关联的同时,进一步细化了分析的颗粒度和大幅提升了自动化水平。当前4.0阶段的标志是生成式人工智能的渐进应用,以DeepSeek为代表的大语言模型技术及智能体应用突破传统规则约束,在保持高准确率的前提下实现全流程自主分析,使政策工具研究的维度拓展、效率提升与知识发现达到新高度。政策工具识别与分类的范式演进体现了从依赖人类经验的碳基范式向依托人工智能的硅基范式转变。该过程既遵循技术发展的客观规律,也深刻影响着政策科学的研究形态。The identification and classification of policy instruments is a foundational topic in policy science research.Through a systematic literature review and historical analysis of technological advancements,this article delineates four distinct eras of policy instrument research,each reflecting advancements in technology and methodological paradigms.In the 1.0 era,researchers relied primarily on qualitative and normative analysis,drawing on personal expertise and experience.With the advent of the 2.0 era,content analysis emerged as the predominant approach,facilitated by computer-assisted systematic text analysis.Software tools like NVivo enabled structured coding and content analysis,significantly enhancing both the efficiency and consistency of analyses.In the subsequent 3.0 era,natural language processing(NLP)models such as WordBERT and neural networks enabled researchers to conduct more sophisticated textual and data analyses.Machine learning and deep learning technologies enriched the analytical depth and level of automation,advancing the precision and intelligence of policy tool identification and classification.The defining feature of the current 4.0 era is the deep integration of generative AI technologies,such as large language models(e.g.,DeepSeek),and intelligent agents.These systems break through rule-based constraints,enabling end-to-end autonomous analysis with high accuracy.This marks a new height in the scale,efficiency,and knowledge discovery potential of policy instrument research.The delineation of these four eras vividly illustrates the historical shift in policy instrument identification and classification paradigms—from carbon-based experience to silicon-based intelligence.Looking ahead,as AI technologies continue to evolve,research in policy instrument identification and classification will enter an era of heightened intelligence and personalization,providing more precise,scientific,and efficient solutions for policy science and public governance research and practice.

关 键 词：政策工具 识别分类 范式演进 数智驱动 人工智能体 

分 类 号：D63[政治法律—政治学]