Situated Epistemic Infrastructures: A Diagnostic Framework for Post-Coherence Knowledge

TL;DR

This paper presents the Situated Epistemic Infrastructures framework as a diagnostic tool to analyze how knowledge credibility is mediated in hybrid human-machine systems under post-coherence conditions, challenging traditional epistemic models.

Contribution

It introduces the SEI framework, integrating infrastructure and epistemology insights, to analyze authority in knowledge systems beyond traditional scholarly boundaries.

Findings

SEI effectively traces credibility mediation across institutional and computational arrangements.

The framework highlights the importance of coordination over classification in epistemic practices.

It offers a new perspective for AI governance and ethical information system design.

Abstract

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.