Where does an LLM decide what’s right? We study whether normative judgment is implemented by a sparse, identifiable substrate or distributed across the model. Conventional circuit analysis can conflate normative computation with the multiple-choice format machinery that surrounds it. We pair four normative datasets (legal and moral, Chinese and English) with a non-normative MMLU control and apply a locate–disentangle–intervene pipeline that sets aside format-processing heads. Across Qwen3-8B, Llama3.1-8B, and Mistral-7B-v0.1, this isolates 5–10 normative-specific (NS) heads per model (under 1% of total) in MCQA settings. Targeted knockout and amplification reveal bidirectional causal effects across legal and moral tasks. The resulting heads form a sparse, late-layer intervention substrate for normative MCQA. Their coordinates differ across model families, so cross-model agreement is structural and circuit findings require functional re-identification in each family.