A Legibility-First Interpretive Framework for Cross-Domain Classification Under Uncertainty: A Coordinated Six-Document Set

This Zenodo deposit presents a coordinated six-document set that defines, specifies, and demonstrates a legibility-first interpretive framework for cross-domain classification under conditions of uncertainty. The contribution is explicitly bounded and architectural. The work does not claim to introduce new ontological primitives, empirical laws, or completed validation programs. Instead, it integrates established analytic resources—drawn from ontology, control and cybernetic reasoning, governance and review logic, validity theory, and interpretive disciplines—into a single portable framework that preserves separation among observation, interpretation, confidence, action, and revision. The framework is designed to prevent category collapse, premature closure, hidden threshold drift, and revision-by-overwrite by enforcing protected analytic distinctions, an ordered admissibility structure, explicit threshold logic, and continuity-preserving revision governance. It is intended for use across domains including AI evaluation, institutional oversight, clinical interpretation, and education, without requiring shared metrics or domain-specific ontologies. The deposit contains the following documents: Legibility-First Interpretive Framework StatementDefines the framework, its scope, bounded claim, and core architecture. Protected Distinctions in Legibility-First InterpretationSpecifies the anti-collapse grammar and the separation discipline required for structured interpretation. Admissibility, Revision, and Continuity-Preserving UpdateStates the ordered logic by which claims, actions, and revisions become licensable. Contribution, Lineage, and Bounded OriginalityClarifies what is inherited, what is recombined, and what constitutes the author’s architectural contribution. Worked Examples and Coding CasesDemonstrates how the framework changes classification outcomes within its own analytic terms. Retrospective Comparative Validation of the Legibility-First FrameworkProvides a bounded comparative analysis showing how the framework alters classification behavior relative to ordinary handling on anchored retrospective cases. Together, these documents establish a citable, cross-domain interpretive architecture intended to support more legible, reviewable, and continuity-preserving decision processes under uncertainty. Keywords interpretive framework classification under uncertainty ontology and governance decision theory (applied) AI evaluation institutional oversight clinical reasoning category collapse admissibility logic revision governance cross-domain analysis Ψ(x) = ∇ϕ(Σ𝕒ₙ(x, ΔE)) + ℛ(x) ⊕ ΔΣ(𝕒′) — C077UPTF1L3 / C.W.Copeland Licensed CRHC v1.0