This talk will focus on the contrast between aggregation of individual preference rankings to a collective preference ranking and aggregation of individual value judgments to a collective value judgment. The targeted case is one in which value judgments also have the form of rankings.  Despite of this formal similarity, the kind of aggregation procedure that works fine for judgments - minimization of distance from individual inputs - turns out to be inappropriate for preferences. Whatever measure of distance is chosen, distance-based procedures violate the strong Pareto condition. Which seems alright as value judgment aggregation goes, but wouldn't be acceptable for preference aggregation, on the most natural interpretation of the latter task. Distance-based aggregation of value judgments might also be approached from the epistemic perspective: questions might be raised about its advantages as a truth-tracker. From this perspective, what matters is not only the probability of the output being true, but also the expected verisimilitude of the output, i.e. its expected distance from truth.