Scholars have used both quantitative and qualitative approaches to empirically study nonprofit roles. Mission statements and program descriptions often reflect such roles, however, until recently collecting and classifying a large sample has been labor-intensive. This research note uses data on United Ways that e-filed their 990 forms and supervised machine learning to illustrate an approach for classifying a large set of mission descriptions by roles. Temporal and geographic variation in roles detected in mission statements suggests that such an approach may be fruitful in future research.
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