Situated Epistemic Infrastructures: A Diagnostic Framework for Post-Coherence Knowledge
This work addresses the challenge of AI governance and knowledge production for researchers and policymakers by offering a novel diagnostic framework, though it is incremental in building on existing theories.
The paper tackles the problem of how knowledge becomes authoritative in hybrid human-machine systems under post-coherence conditions, introducing the Situated Epistemic Infrastructures (SEI) framework as a diagnostic tool to analyze credibility mediation across institutional, computational, and temporal arrangements.
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.