探索向数据驱动软科学研究范式转型

Core Viewpoint - The article discusses the transition from expert-driven to data-driven research paradigms in soft science, emphasizing the importance of digital technologies in enhancing decision-making quality and efficiency through data analysis [1][2]. Group 1: Transition from Expert-Driven to Data-Driven Paradigm - The traditional expert-driven paradigm relies on literature review and theoretical analysis to formulate research hypotheses, while the data-driven paradigm focuses on analyzing large datasets to uncover underlying patterns and relationships [1]. - The shift to a data-driven approach faces challenges such as inertia from the expert-driven model and the need for collaboration among data methods, digital talent, and data resources [1][2]. Group 2: Pathways for Transition - The transition requires a dual-driven model that combines data analysis with expert knowledge to enhance research rigor and relevance [2]. - Establishing a consensus on data-driven approaches involves integrating data thinking into organizational strategies and fostering a culture that values data-driven insights [3]. Group 3: Building Research Capacity - Strengthening the research team is essential, focusing on developing data capabilities and attracting interdisciplinary talent with expertise in both data science and social sciences [3][4]. - A robust data management mechanism is necessary to support effective data utilization, including the creation of specialized databases and promoting data sharing across institutions [4]. Group 4: Innovation in Methods and Models - Accelerating the development of algorithms and intelligent analysis models is crucial for transforming research processes, integrating AI technologies to enhance quantitative and qualitative research [5]. - Promoting interdisciplinary methods and tools will help break down barriers between different fields, allowing for a more comprehensive approach to data analysis [5].