Sample-level ranking is widely used for data selection, debugging, and training-data attribution, but redundant NLP corpora—exact and near duplicates, paraphrases, and templated examples—can make proxy-induced rankings unstable across random seeds. We study stable sample-level ranking under redundancy and propose SCARV, a modular aggregation layer that operates on top of existing scoring proxies. SCARV normalizes scores within each run, can regularize redundant examples through cluster-level aggregation and allocation, and aggregates across seeds with a robust operator. Across synthetic redundancy, naturally mined QQP redundancy, multiple proxy families, classification and sentence-pair tasks, and end-to-end DistilBERT fine-tuning, stability-aware aggregation improves over bare proxy rankings and yields more reproducible ranking-based decisions, such as selected subsets and suspicious-example sets. Matched mechanism analysis shows that cross-seed aggregation is the dominant generic stabilizer. The incremental effect of structural regularization is small, map-dependent, dataset-dependent, and sometimes negative; a meaningful redundancy map is useful mainly when group-consistent treatment is operationally desired. These results position SCARV as a stability-oriented aggregation framework rather than a universal data selector and emphasize decision reproducibility over uniform gains in downstream utility.