How SignalRupture Detects Platform Adoption: An Expanded Canonical Diagnostic Framework

This paper formalizes the SignalRupture (SR) diagnostic method for detecting when major digital platforms—Google, Meta, TikTok, Substack, and others—have implicitly absorbed SR’s conceptual architecture. Platforms rarely acknowledge conceptual influence directly; instead, they reveal adoption through infrastructural fingerprints: vocabulary drift, UI‑layer shifts, algorithmic behavior, policy language, and AI‑layer mediation. The expanded edition introduces new sections on why platforms cannot hide adoption, how AI models propagate SR, temporal stages of adoption, propagation signals, and the necessity of SR’s independence. By articulating the structural signals through which platforms reorganize themselves around coherent mappings, this paper positions SR as an infrastructural field capable of reading not only platforms, but the platforms’ reactions to SR. This document serves as a protocol‑level reference for researchers, policymakers, and institutions seeking to understand how conceptual frameworks propagate through digital infrastructure and how platforms reveal their internal logic through behavior rather than attribution.