In Generative Information Retrieval (GenIR), the bottleneck has shifted from generation to the selection of candidates, particularly for normative criteria such as cultural relevance. Current LLM-as-a-Judge evaluations often suffer from circularity and preference leakage, where overlapping supervision and evaluation models inflate performance. We address this by formalising cultural relevance as a within-query ranking task and introducing a leakage-free two-judge framework that strictly separates supervision (Judge B) from evaluation (Judge A). On a new benchmark of 33,052 (NGR-33k) culturally grounded stories, we find that while classical baselines yield only modest gains, a dense bi-encoder distilled from a Judge-B-supervised Cross-Encoder is highly effective. Although the Cross-Encoder provides a strong supervision signal for distillation, the distilled BGE-M3 model substantially outperforms it under leakage-free Judge~A evaluation. We validate our framework on the human-curated Moral Stories dataset, showing strong alignment with human norms. Our results demonstrate that rigorous evaluator separation is a prerequisite for credible GenIR evaluation, proving that subtle cultural preferences can be distilled into efficient rankers without leakage.

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