Abstract

ABSTRACT The empirical study of virtue is plagued by imprecise definitions and assessment. Here we propose a three-stage, data-driven (‘bottom-up’) method to differentiate lay perceptions of virtues. Employing two virtues – generosity (as cooperation) and fairness (as impartiality) – as a case study, we present findings utilizing data from three studies (total N = 2,667). First, natural language processing of free-response data indicated that participants used different ‘topics’ (i.e. clusters of words) to describe behaviours representing generosity (topics: ‘charity’ and ‘kindness’) and fairness (‘equality’). Second, participants in a survey experiment rated behaviours expressing generosity and fairness differently across 6 out of 9 underlying features measured. Third, participants perceive that actors in vignette-based experiments engaging in behaviours expressing generosity versus fairness were motivated differently on 5 out of 6 motivations measured. Our findings support the distinction of the virtues of generosity (as cooperation) and fairness (as impartiality) and indicate the utility of our bottom-up method for assessing and distinguishing virtues.

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