Hate speech classifiers often misclassify counter speech, including condemnation and quotation of hateful content, as hateful because they fail to distinguish propositional content from speaker stance. We identify this issue as content stance conflation and address it as a representation learning problem. We learn stance invariant representations of the same proposition by constructing contrastive pairs that share content but differ in stance. We implement this idea through factorized annotation, stance controlled augmentation, and Stance Contrastive Learning (SCL). On the Implicit Hate Corpus, stance variant augmentation reduces counter speech false positives by 60%, and SCL further improves cross domain F1 by up to 6.5 points on top of the augmented signal. On SBIC, where use mention conflation is less prominent in the training labels, SCL maintains a low counter speech false positive rate. Semantic similarity pairing on the same augmented data yields no cross domain gain over the model trained without contrastive learning (0.623 vs. 0.625), which identifies the pair construction signal rather than contrastive learning itself as the determining factor.