Evidence Contracts for Domain-Mapped Prediction

A predictive feature needs not be admissible evidence for the interpretation attached to an output. We formalize source-qualified evidence contracts that constrain a semantic model while preserving an unrestricted predictor. Positive, negative, unknown and conflicting observations compile to permitted concept completions. We distinguish expected-band invariance from probability certification and prove a conditional error bound under concept-dependent reporting shift, with a counterexample showing why ordinary calibration is insufficient. In a five-seed replication, probability certificates achieve maximum mean selective error of 1.66% across seven correct-contract regimes, compared with 4.19% for a same-contract MLP and 3.08% for a typed MLP at matched coverage; source-like error is slightly worse. Exhaustive checks show that fitted heads do not satisfy the theorem's pointwise assumption, so the empirical result is not promoted to a nominal guarantee. A text-to-contract diagnostic exposes extraction errors, and a semi-synthetic study on 5,070 real questionnaire records establishes an exact arithmetic baseline. Together, these results separate admissible evidence, assumptions for reporting robustness and measured performance.