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Formal rankings arise from algorithms that incorporate multiple inputs into single metrics, and a recurring criticism is that they impose excessive uniformity in evaluations. A less explored possibility is that decontextualized metrics encourage greater heterogeneity in the inferences that users draw. Across three experiments, I investigate the inequality implications of this possibility in the context of educational rankings. Study 1 shows that rankings are perceived as stronger signals of exclusivity, academic rigor, and beneficial social ties for those from higher socioeconomic status (SES) backgrounds, while serving as stronger signals of exclusion (i.e. unwelcoming) for those from lower SES backgrounds. Study 2 demonstrates that the introduction of numeric rankings exacerbates – and not only merely reflects – these differences in interpretation. Study 3 shows that inconsistent decoding can be inaccurate, as when adolescents whose parents lack a college degree incorrectly perceive rankings as a significantly stronger signal for the net price of a college than their more advantaged peers. Across these studies, these SES differences in what people infer from rank explain self-sorting, where those from higher SES backgrounds prefer prestigious colleges significantly more than those from lower SES backgrounds.