Collective Mastery Inference (CMI) is a purely theoretical, downstream extension of Unified Mastery Theory (UMT) that constrains when claims of collective mastery are epistemically admissible. Rather than proposing metrics or evaluative procedures, CMI formalizes the inferential limits that arise at collective scales due to non-compositionality, identifiability collapse, counterfactual fragility, emergent failure modes, and temporal drift. The framework demonstrates why aggregate performance, averaged individual competence, and scalar or rank-based measures cannot license collective mastery attribution, and it specifies regimes in which such claims are inadmissible or impossible in principle. By reducing claim space instead of expanding it, CMI preserves theoretical closure within the UMT ecosystem and prevents silent misuse of mastery concepts at scale.
Murad Ahmadov (Thu,) studied this question.