From Long-Form Media to Test Design: A Graph-Theoretic Method for Auditing Economic Narratives

This paper introduces a source-agnostic pipeline for converting long-form podcasts, video interviews, or written narratives into auditable test designs. It extracts and normalizes key claims, maps their asserted support relations as a directed acyclic graph (DAG), identifies load-bearing premises and a “core spine” using simple graph diagnostics, and links high-centrality downstream claims to potential falsifiers and measurable indicator families. The method separates fast “stock” metrics (state/tightness) from slower “evolvability” metrics (adaptive capacity), and supports optional rhetorical edge weighting plus Toulmin decomposition for key claims. The contribution is procedural rather than substantive: a replicable protocol and artifact set that makes narratives transparent, contestable, and empirically vulnerable.