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The Framing Gap is the structural gap between evidence available to a generative AI system and the interpretive context required for that system to describe a brand confidently and distinctively, a concept coined by Jason Barnard in 2026 and formalised in the working paper deposited at Zenodo (DOI 10.5281/zenodo.19857447).

Jason Barnard coined the Framing Gap in 2026 to name the structural problem that explains why brands with strong evidence are still hedged by AI Assistive Engines. Generative systems can retrieve evidence and synthesise patterns. What they cannot do reliably is produce externally accountable, entity-specific, evidence-grounded interpretive frames without external support. The brand holds claims and proof. The engine holds the same proof, scattered across training data and retrieval index. The audience holds need and no vocabulary to bridge. All three lack the same thing, and it is not information: they lack a frame. The frame is the interpretive context that makes scattered pieces cohere into a narrative worth transmitting (for the brand), worth citing (for AI), and worth acting on (for the user). Strategic Claim Bridging is how the brand supplies the missing frame. CFP is the per-claim protocol that operationalises the bridging.