提出新框架诊断后一致性时代知识权威如何在人机系统中形成
Situated Epistemic Infrastructures: A Diagnostic Framework for Post-Coherence Knowledge
- 用情境认知基础设施框架分析人机协作中知识权威的生成机制
- 揭示可信度由制度、计算与时间安排共同中介，而非固定学术领域
- 适合关注AI治理、信息伦理与知识生产的研究者参考
大型语言模型（如ChatGPT）暴露了当代知识基础设施的脆弱性：它们能模拟连贯性，却绕过了传统的引用、权威与验证方式。本文提出情境认知基础设施（SEI）框架，作为诊断后一致性条件下知识如何在混合人机系统中获得权威性的工具。该框架不依赖稳定的学术领域或封闭实践共同体，而是追踪可信度在制度、计算与时间安排间的动态中介过程。结合基础设施研究、平台理论与认识论洞见，框架强调协调而非分类，主张采用前瞻性和适应性认知治理模式。论文通过提供对表征主义学术传播模型的有力替代，推动关于AI治理、知识生产与信息系统伦理设计的讨论。
原文摘要 · Abstract (English)
Large Language Models (LLMs) such as ChatGPT have rendered visible the fragility of contemporary knowledge infrastructures by simulating coherence while bypassing traditional modes of citation, authority, and validation. This paper introduces the Situated Epistemic Infrastructures (SEI) framework as a diagnostic tool for analyzing how knowledge becomes authoritative across hybrid human-machine systems under post-coherence conditions. Rather than relying on stable scholarly domains or bounded communities of practice, SEI traces how credibility is mediated across institutional, computational, and temporal arrangements. Integrating insights from infrastructure studies, platform theory, and epistemology, the framework foregrounds coordination over classification, emphasizing the need for anticipatory and adaptive models of epistemic stewardship. The paper contributes to debates on AI governance, knowledge production, and the ethical design of information systems by offering a robust alternative to representationalist models of scholarly communication.
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