Symbolic Diagnosticity under Symbol Inflation

Symbolic forms allow social actors to infer otherwise inaccessible histories of authorship, effort, expertise, attention, commitment, and institutional authorization. This paper examines how these inferences change as increasingly heterogeneous generative processes produce comparable observable manifestations. Symbol inflation is approached as a domain-dependent decoupling of proliferating symbolic forms from the relational, evidential, and institutional grounds of their significance; its operational criteria differ across research domains. Generative artificial intelligence is an accelerator within a longer history of mediation. The paper advances a target-relative theory of diagnosticity and distinguishes generative depth from identifiability, stability, and informational content in inverse reconstruction. Nonlinear mediation can preserve an originating history, collapse different histories into a common surface, or amplify observational uncertainty. Coupled feedback can alter stable interpretive states and decisions. Such results depend on the transformations, target properties, distributed inputs, recipient information, and decision conditions. Constructed mathematical examples establish conditional mechanisms, while the empirical programme separates actual production, disclosure, recipient judgments, and consequential reliance. Across communication, expertise, education, provenance, cultural value, and credibility allocation, the analysis avoids assuming a uniform decline in trust. Symbolic crisis names a broader disturbance of historically established symbolic mediation, including the relations among meaning, recognition, subject formation, desire, and value. Consequential diagnostic mismatch is examined as one empirically tractable mechanism within that larger problem.