Joint-encoding zero-shot relation extraction classifies all candidate relation labels in a single Transformer forward pass, achieving strong accuracy and high throughput. However, under a controlled full-vocabulary diagnostic, two distributional pathologies surface: unnormalized dot-product scoring causes recall collapse, with certain types receiving zero predictions, while the per-instance BCE loss leaves gold and negative scores entangled beyond any reliable threshold—confidence ambiguity. We propose SCAR, which keeps the baseline pathway intact and stabilizes both distributions via two auxiliary branches: a gradient-isolated coarse-to-fine cascade with Z-score normalization that restores cross-relation comparability, and a training-only contrastive alignment module that separates gold from negative scores. On Wiki-ZSL (m=15), SCAR raises Macro F1 from 75.9% to 83.0% and cuts zero-recall types from 35 to 1, suggesting that small-pool ZSRE protocols can obscure failure modes critical to large-vocabulary deployment.