We describe the AI4PC–Howard University submission to SemEval-2026 Task 9, Subtask 1 on multilingual political polarization detection across 22 languages. We investigated two approaches: (1) a weakly supervised teacher–student framework in which a large language model (LLM) generated pseudo-labels to train an XLM-RoBERTa-base classifier, and (2) (2) a context-engineered prompt-based approach using Meta-Llama-3.1-8B-Instruct. The teacher–student approach exhibited instability under distribution shift and collapsed toward majority predictions at test time. Consequently, our final submission used direct inference with Meta-Llama-3.1-8B-Instruct. While this approach produced competitive macro-F1 across evaluated languages, results reveal strong positive-class bias and substantial precision–recall imbalance. Our findings highlight limitations of weak supervision for subjective political tasks and underscore trade-offs between scalability, bias, and computational cost in LLM-only multilingual systems.