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Automated coding using machine-learning and remapping the U.S. nonprofit sector: A guide and benchmark

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  • Ma, Ji

    (The University of Texas at Austin)

Abstract

This research developed a machine-learning classifier that reliably automates the coding process using the National Taxonomy of Exempt Entities as a schema and remapped the U.S. nonprofit sector. I achieved 90% overall accuracy for classifying the nonprofits into nine broad categories and 88% for classifying them into 25 major groups. The intercoder reliabilities between algorithms and human coders measured by kappa statistics are in the "almost perfect" range of 0.80--1.00. The results suggest that a state-of-the-art machine-learning algorithm can approximate human coders and substantially improve researchers' productivity. I also reassigned multiple category codes to over 439 thousand nonprofits and discovered a considerable amount of organizational activities that were previously ignored. The classifier is an essential methodological prerequisite for large-N and Big Data analyses, and the remapped U.S. nonprofit sector can serve as an important instrument for asking or reexamining fundamental questions of nonprofit studies.

Suggested Citation

  • Ma, Ji, 2020. "Automated coding using machine-learning and remapping the U.S. nonprofit sector: A guide and benchmark," OSF Preprints pt3q9, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:pt3q9
    DOI: 10.31219/osf.io/pt3q9
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    1. Valerio Baćak & Edward H. Kennedy, 2019. "Principled Machine Learning Using the Super Learner: An Application to Predicting Prison Violence," Sociological Methods & Research, , vol. 48(3), pages 698-721, August.
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