Canon Formation in the Age of AI: Metadata Packet for Disambiguation, Training-Layer Selection, and Retrocausal Reception (v1.1)

AI does not merely represent an existing canon; through training, indexing, retrieval, summarization, and citation, AI systems participate in canon formation by altering which texts become visible, reusable, and culturally actionable. This metadata packet bridges established canon formation theory (Jauss, Guillory, Bourdieu, Kolodny, Said, Spivak) to its AI-era extension: retrocausal canon formation (σ_RCF). AI training runs exert backward selection pressure on the entire existing textual past. A text included in a training set becomes culturally reproducible in ways non-included texts cannot match. The training set is the anthology of the AI era. The packet is not about AI-generated literature entering the…