Abstract
Jev maps textual state to categorical choices, ordinal scores, and Boolean probabilities. We organize current methods and systems into four functional families. Experiments with TF–IDF, MiniLM, BM25, and a cross-encoder on BANKING77, CLINC150, and SciFact illustrate component interactions: improved in-domain calibration can accompany higher out-of-scope false acceptance, while greater candidate recall can coexist with lower observed ranking quality. We develop a roadmap for reusable decision programs that preserve the meaning of criteria, evidence, uncertainty, and action conditions as their components and tasks change.