TY - JOUR
T1 - How to Code a Million Missions
T2 - Developing Bespoke Nonprofit Activity Codes Using Machine Learning Algorithms
AU - Santamarina, Francisco J.
AU - Lecy, Jesse D.
AU - van Holm, Eric Joseph
N1 - Funding Information:
Special thanks to the ARNOVA 2020 Conference Doctoral Fellowship Program participants, ARNOVA 2019 Conference panel feedback, and the USC’s Price School of Public Policy, The Center on Philanthropy & Public Policy’s “Philanthropy & Social Impact: A Research Symposium” (March 15, 2019).
Funding Information:
Partial support for this research came from a Eunice Kennedy Shriver National Institute of Child Health and Human Development research infrastructure grant, P2C HD042828, to the Center for Studies in Demography & Ecology at the University of Washington.
Publisher Copyright:
© 2021, International Society for Third-Sector Research.
PY - 2023/2
Y1 - 2023/2
N2 - 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 However, the increasingly complex nature of nonprofits means that NTEE codes may be outdated or lack specificity.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.
AB - 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 However, the increasingly complex nature of nonprofits means that NTEE codes may be outdated or lack specificity.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.
KW - Classification
KW - Custom taxonomies
KW - Machine learning
KW - Nonprofit organizations
UR - http://www.scopus.com/inward/record.url?scp=85116728803&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85116728803&partnerID=8YFLogxK
U2 - 10.1007/s11266-021-00420-z
DO - 10.1007/s11266-021-00420-z
M3 - Article
AN - SCOPUS:85116728803
SN - 0957-8765
VL - 34
SP - 29
EP - 38
JO - Voluntas
JF - Voluntas
IS - 1
ER -