We present our system for StanceEval-2026 Track 1, which focuses on Arabic stance detection toward Women Driving using the Mawqif-v2 dataset with three labels: Favor, Against, and None. We compare fine-tuned AraBERT models with zero-shot Gemma-4-31B. We refine the Gemma prompt using recurring development-set errors, focusing on dialect and indirect meaning, target identification, sentiment versus stance, hashtags, sarcasm, and the distinction between reporting and endorsement. We also compare the calibrated system with several alternative inference strategies, including self-consistency, model debate, and dialect-interpreter pipelines. The final Gemma-4-31B system achieves 86.61% Favg2, 67.46% Favg3, and 82.67% accuracy on the official blind test, ranking 5th. Our results show that target-specific context and clear decision rules can be more effective than adding additional inference stages or model interactions. The main remaining challenge is distinguishing Against from None, particularly when stance is indirect, ambiguous, or expressed through humor.