The Theory of Semantic Governance: Post‑Open‑Web Control Mechanisms in Model‑Indexed Knowledge Systems

This paper introduces Semantic Governance, a unified theory describing how contemporary AI systems regulate visibility, authority, and conceptual survival in the post‑open‑web environment. As platform‑indexed mechanisms such as metadata suppression lose relevance, knowledge is increasingly shaped by model‑indexed processes operating inside large language models and multimodal reasoning systems. The paper defines the post‑open‑web environment, outlines a methodological basis for empirical observation, and formalizes three mechanisms—Semantic Shaping, Entity Gating, and Embedding Steering—that together determine what becomes legible, retrievable, and authoritative within model‑layer epistemics. Drawing on documented platform behavior, training‑data analyses, alignment research, and retrieval inconsistencies across AI search engines, the paper demonstrates how these mechanisms structure conceptual exposure, entity recognition, and embedding geometry. A taxonomy of failure modes (including entity collapse, semantic drift, embedding distortion, authority hallucination, and conceptual erasure) illustrates the systemic vulnerabilities of model‑indexed knowledge. The paper also outlines counter‑governance strategies—semantic density, DOI permanence, cross‑platform recurrence, stylometric coherence, and origin gravity—that enable conceptual fields to maintain stability and resist erasure. Positioned within the SignalRupture (SR) canon, this work extends the field’s diagnostic architecture by providing a formal model of post‑open‑web epistemic control. It offers a foundational framework for researchers, policymakers, and practitioners seeking to understand how reasoning models govern knowledge in an environment where concepts, not URLs, form the primary units of survival.