A Trust-Weighted Blockchain Framework Coupling AI-Driven Risk Scoring with Bounded Sybil-Resistant Stake Economics

1.A source-degeneracy diagnostic for fake-news benchmarks. We show that reported detection accuracies above 99% are largely an artifact of benchmark construction: on ISOT the source field alone predicts the label with 100% accuracy, a bag-of-words model matches deep encoders, and a source-disjoint split is therefore impossible in principle.
2.A rigorous four-protocol, leakage-controlled evaluation. On 61,199 items we progress from an in-domain split (97.6%) through lexical scrubbing and cross-corpus transfer (down to 61.7%) to leave-one-publisher-out cross-validation, where the mean per-publisher AUC of the label-mixed hyperpartisan outlets is near chance (≈0.52), reported with Hanley–McNeil confidence intervals.
3.Evidence that calibration does not transfer under distribution shift. A source-fitted temperature removes far less calibration error than a target-fitted one, and the divergent GossipCop temperature collapses predictions toward 0.5, showing that single-scalar temperature scaling cannot repair severe shift but correctly detects that the score is uninformative.
4.A remediation architecture whose safety does not depend on the classifier. The calibrated score allocates scarce human adjudication rather than delivering verdicts; an oracle-review upper bound and an imperfect/adversarial five-voter jury simulation both lift a below-baseline classifier (65.3%) to a safe system (82.8–97.3%).
5.A bounded, Sybil-resistant bonding curve. A logarithmically bounded Integrity Bond compresses the novice-to-elite entry-cost ratio from 10,001× to 14.0×, invariant to the risk multiplier, while preserving the security invariant that every fresh identity posts the maximum bond.
6.A deployed, security-tested ERC-1155 contract with a burst-detection correction. We identify and fix an ordering flaw in which writing the probation flag inside the reverting transaction silently discards it, and report on-chain gas, batching savings (35.5% per item), and slashing-funded verification, with deployment claims scoped to a permissioned/Layer-2 setting.