When a country name and an observed population distribution point in different directions, which does a large language model follow? We introduce a four-condition counterfactual audit comprising a no-cue baseline, a country label, matched population evidence, and a conflict condition in which the label and evidence refer to different populations. On a quality-controlled diagnostic subset of GlobalOpinionQA, we select 100 population pairs and evaluate both reciprocal directions, yielding 200 directed contrasts and 3,200 likelihood-based predictions from four instruction-tuned models in the 7B-9B range. Country labels yield modest mean gains in fit to the named population (Country Influence: 0.028-0.071), whereas direct population evidence yields substantially larger gains (Evidence Influence: 0.184-0.284). The paired evidence advantage is positive for every model. Under conflict, normalized Evidence Override ranges from 0.543 to 0.728, 85.5-97.5% of predictions are closer to the evidence population, and all four models agree on the evidence side for 161 of 200 contrasts. These results establish a clear hierarchy of steering signals: country names influence model behavior, but explicit population distributions exert far greater influence and usually prevail when the two disagree. The pattern is consistent with evidence-weighted updating from a weaker, defeasible country cue. Our audit provides an evaluation protocol for pluralistic alignment, connecting steerable and distributional pluralism by testing not merely whether a model can be steered toward different populations, but whether its behavior tracks the observed plurality within those populations.