The Provenance-Recognition Decoupling Principle (PRDP)

This manuscript develops the Provenance-Recognition Decoupling Principle (PRDP), aproposed framework for investigating circumstances in which documented evidence aboutthe origin or priority of a contribution does not align with the recognition subsequentlyallocated to its contributor. The framework distinguishes five analytically separate constructs:provenance evidence, chronological priority, discoverability, visibility, and recognition. It doesnot assume that earlier publication proves conceptual originality, that network centralitynecessarily produces credit, or that previously neglected work will inevitably be recognizedlater.The proposed architecture contains seven analytical layers and two principal branches.The short-term branch examines structural divergence during a prespecified observationwindow; the long-term branch examines recognition reconfiguration, persistence, recovery, andcorrection over a longer or otherwise consequential transition process. Time is treated as oneanalytical dimension rather than the sole mechanism. Network topology, access conditions,institutional structures, language, geography, algorithmic mediation, evidence quality, andcorrective institutions are represented as potentially distinct factors.The manuscript defines operational constructs, a provenance-recognition rank-discordancestatistic, a network representation, candidate probability models, a causal analysis plan, fiveprincipal hypotheses, explicit counterexamples, a fourteen-step audit protocol, and thirtycomparative case-study designs. Two principal cases are specified: the Non-ConnectedGenesis Case and the Delayed Provenance Recognition Case. The thirty comparative casesare research designs rather than claims that thirty documented empirical studies have alreadybeen completed.The contribution is therefore a formalized and falsifiable research program, not an estab-lished universal empirical law. Its validity depends on construct validity, source verification,model identifiability, comparative testing, robustness analysis, and independent replication.The manuscript also identifies relevant prior scholarship on scientific priority, cumulativerecognition, network dynamics, and data provenance. The proposed framework's originalityrelative to those literatures remains subject to a more comprehensive systematic review.