Reliability-Aware Temporal Multiview Learning Under Distribution Shift: Direct-Identifier-Free Prediction of Unsuccessful Public Tenders

Longitudinal administrative prediction is vulnerable to temporal leakage, institutional shortcut learning, and distribution shift. We evaluate a reliability-aware multiview framework for predicting unsuccessful public tenders using 176,470 finalized Mongolian procurement procedures (2016–2025). Initial training covers 2016–2022, development 2023, and forward evaluation 2024–2025. Current-announcement predictors are separated from historical inputs with conditional public-availability assumptions. Prior buyer-announcement histories improved PR-AUC by 0.02635 in 2024 and 0.02236 in 2025 over current structured attributes, while buyer-disjoint retraining did not establish a reliable benefit from explicit institutional identifiers. Expanded-feature domain classifiers distinguished 2023–2024 from 2024–2025 (ROC-AUC 0.896 versus 0.670), but retained a possible procurement-coding proxy; a stricter coding-excluded diagnostic showed substantially weaker discrimination. Conditional Structured + Text fusion attained PR-AUC values of 0.3269 (2024) and 0.3236 (2025). Transformer augmentation improved the 2024 ROC-AUC by 0.00749 (95% buyer-cluster CI 0.00297–0.01222), whereas its PR-AUC increments were not statistically established in either forward year. Separate calibration, selective-deferral, and conformal analyses revealed reliability limitations under temporal change. Independent replays verified saved-model inference, development fusion coefficients, displayed forward metrics, and clustered bootstrap conclusions. The Structured + Text reporting reference was selected retrospectively for stability, not confirmatory architectural superiority; historical first-publication constraints remain essential for deployment.