COGNITIVE-STRUCTURAL ANALYSIS (CSA)

Cognitive-Structural Analysis (CSA) v1.3
CSA is a multi-layer coherence framework for analyzing stability, strain, and reintegration across systems that unfold through sequential states. It applies to stories, fiction, non-fiction, and social constructs, treating each as a coherence architecture governed by five interacting layers: the Baseline Coherence Layer (BCL), Regression-Sequence Layer (RSL), Cross-Lifetime Integration Layer (CLIL), Disruption Cascade Layer (DCL), and Hoffman-State Encroachment Layer (HSEL).

This whitepaper formalizes the layer model, introduces the Coherence Gradient (CG), Fragmentation Index (FI), and Integration Coefficient (IC), and provides templates for practical application. CSA offers a unified method for mapping how coherence is maintained, disrupted, or reconstituted across narrative, psychological, relational, and sociocultural systems. Version 1.3 is the canonical edition.

Cognitive-Structural Analysis (CSA) v1.3.1

Added AI attribution: Portions of this document were drafted or refined using OpenAI’s ChatGPT under the author’s direction. All theoretical frameworks, concepts, equations, and methodological structures originate from the author.