This paper introduces the Standard Coherence Fidelity Layer (SCFL), a theory-agnostic measurement framework that quantifies structural organization in representational systems across biological and artificial substrates. SCFL defines four formal observables — coherence fidelity (CF), drift rate (DR), rupture index (RI), and recoverability time (RT) — derived from time series of system representations Rₜ ∈ ℝ^ (N×D). Formal definitions employ Procrustes distance, KL-divergence, and change-point detection. A Python reference implementation using SciPy and Scikit-learn is provided. The framework is validated against neural data from anesthesia-induced state transitions and artificial system perturbations in transformer architectures. SCFL complements existing consciousness metrics (PCI, LZC, Φ) by measuring stability and recoverability rather than complexity alone, enabling cross-theory comparison across Integrated Information Theory and Global Neuronal Workspace Theory. Five falsifiable hypotheses predict state-specific coherence trajectories across conscious, sedated, anesthetized, and disrupted-recovery states. The framework is substrate-independent, mathematically grounded, and designed for longitudinal and cross-domain application.
Ronald Brogdon (Wed,) studied this question.