Recognition Networks and Value Accessibility: Detection, Constitution, and Future Opportunity

A contribution can remain difficult to evaluate, difficult to translate, or unable to reach the institutions that allocate future opportunities. These conditions are often compressed into the statement that value lacks recognition. This paper proposes a typed account of recognition accessibility. It separates standard-relative qualification, favorable evaluation, constituted standing, attribution, transmissible evidence, and opportunity-gate access. Recognition can detect a qualification specified under a declared standard, constitute a social or institutional status, or link both layers in a hybrid mechanism. A route decomposition distinguishes exposure, translation, judgment, and attributable transmission. Under conditionally independent evaluators, exact formulas give network sensitivity, false-positive access, and the marginal effect of another evaluator. A shared route barrier creates a strict accessibility ceiling, which separates nominal evaluator count from effective evaluator diversity. A discrete-time hazard identity represents first recognition over a finite horizon. A directed opportunity graph then defines reachable gates and a recognition cut number. Portable evidence weakly enlarges reachability and improves cut robustness only when it bypasses prior bottlenecks. Finally, a two-model construction shows that a finite recognition trace can have the same distribution under a detection model with an unobserved qualification and a constitution model without that variable. Recognition observations therefore require an explicit value ontology and discriminating evidence. Studies of academic judgment, delayed citation, novelty, and interdisciplinary funding motivate the problem while remaining tied to their own indicators and institutions. The resulting framework is a revisable conceptual model. Empirical use requires calibrated standards, dependence analysis, longitudinal network evidence, rival-mechanism tests, and separate evaluation of accuracy, distribution, participation, contestability, privacy, and institutional dependency.