How to Code a Million Missions: Developing Bespoke Nonprofit Activity Codes Using Machine Learning Algorithms

Francisco J. Santamarina, Jesse D. Lecy, Eric Joseph van Holm

    Research output: Contribution to journalArticlepeer-review

    1 Scopus citations

    Abstract

    National Taxonomy of Exempt Entities (NTEE) codes have become the primary classifier of nonprofit missions since they were developed in the mid-1980s in response to growing demands for a taxonomy of nonprofit activities (Herman in Nonprofit and Voluntary Sector Quarterly 19(3):293–306, 1990, Barman in Social Science History 37:103–141, 2013). However, the increasingly complex nature of nonprofits means that NTEE codes may be outdated or lack specificity. As an alternative, scholars and practitioners can create a bespoke taxonomy for a specific purpose by hand-coding a training dataset and using machine learning classifiers to apply the codes to a large population. This paper presents a framework for determining training set sizes needed to scale custom taxonomies using machine learning algorithms.

    Original languageEnglish (US)
    JournalVoluntas
    DOIs
    StateAccepted/In press - 2021

    Keywords

    • Classification
    • Custom taxonomies
    • Machine learning
    • Nonprofit organizations

    ASJC Scopus subject areas

    • Business and International Management
    • Sociology and Political Science
    • Public Administration
    • Strategy and Management

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