智能时代的媒介体制研究：技术作为结构性力量

在智能化传播机制系统性重构全球传播秩序的背景下，传统以国家为单位、以类型化归纳为目标的比较媒介体制研究正面临着认识论基础与理论适用性的双重挑战。智能传播时代所呈现的不仅是媒介环境的技术变迁，更是一场涉及制度边界、权力格局与治理逻辑的系统性变革。平台基础设施、算法治理体系与数据驱动机制共同改变了“国家—媒体—市场”三元关系的结构分布，使媒介体制的核心变量发生深层次重组。尽管国内外学术场域在标准化测量、社会功能拓展、数字维度更新和非西方国家的适用性反思等方面提出了诸多修补路径，但相关研究仍受制于静态类型化、国家中心主义与西方经验依赖。基于此，必须明确智能技术已成为媒介体制演变的结构性因素，要以批判性继承的方法论重构国家角色、市场结构、政治平行性与新闻专业性的运行逻辑。未来的比较媒介体制研究应当进行三重范式转型：在本体论上从制度类型转向制度逻辑、在认识论上从普遍主义转向语境深描、在分析单位上从国家单位转向平台场域，以真正回应智能时代的结构性挑战。

Against the backdrop of intelligent communication mechanism systematically reconstructing the global communication order, the traditional comparative media system research, which takes countries as units and type induction as its goal, is facing a double challenge of epistemological foundation and theoretical applicability. What the intelligent communication era presents is not only the technological changes of the media environment but also a systemic revolution involving institutional boundaries, power patterns and governance logics: platform infrastructure, algorithm governance system and data-driven mechanism together change the structural distribution of the triadic of state-media-market, causing deep-level reorganization of core variables of media system. Despite the numerous remedial paths proposed by domestic and foreign academic fields in terms of standardized measurement, social function expansion, digital dimension renewal, and reflection in non-Western countries, relevant research is still constrained by static typology, state-centrism, and Western experience dependence. Based on this, it must be that intelligent technology has become a structural factor in the evolution of the media system, and the methodology of critical inheritance should be used to reconstruct the operation logic of national role, market structure political parallelism, and journalistic professionalism. Future comparative media system research should undergo three paradigmatic shifts: in ontology from system types to system logic, in epistem from universalism to context deep description, and in analytical units from national units to platform fields, so as to truly respond to the structural challenges of the AI era.