This paper presents Modulign (DAG-OR) as a unified research program comprising a formal address grammar for observable phenomena, three interoperable registries, a reproducible classification protocol, and a growing empirical corpus now exceeding 3.79 million classified observations across 32 active language editions of Wikipedia. We synthesize twenty prior publications—spanning formal logic, analytic epistemology, philosophy of reference, legal epistemology, evidence law, synthetic content governance, AI certification, privacy governance, empirical validation, observer certification, federated registry governance, error correction, and constitutional compliance for automated evidence—into a single coherent account. We argue that the classification deficit identified across scientific, legal, and journalistic domains is not a tooling problem but a grammatical one: existing systems lack the expressive structure to encode observation, causation, jurisdiction, epistemic status, and provenance within a single address. Modulign resolves this deficit. We report on the formal properties of the system (10 invariants, decidable classification, bounded error rate), its empirical performance (inter-rater reliability across trained classifiers, throughput benchmarks, multilingual coverage), and its demonstrated applications to evidence authentication, Gettier-case dissolution, synthetic content governance, and journalistic epistemology. The paper concludes with an honest accounting of open problems and the conditions under which the system would be falsified.
Vincent Gonzalez (Fri,) studied this question.