We present Nabra, a system submitted to StanceEval-2026 Track 1 for Arabic stance detection. Our approach fine-tunes MARBERTv2 on the Mawqif-v2 dataset, exploring seven configurations including class weighting, regularization, data augmentation via back-translation, resampling strategies, and zero-shot inference with Qwen2.5-7B. Our best model by the primary metric Favg2 is M1 (Baseline), achieving 0.837 on the development set and 0.6704 on the official blind test set. Error analysis reveals that Favor→Against misclassification accounts for 84.6% of errors, driven by negative sentiment words co-occurring with supportive stances. We find that metric choice (Favg2 vs. Favg3) significantly impacts model selection decisions.