Automated Coding Using Machine Learning and Remapping the U.S. Nonprofit Sector: A Guide and Benchmark.
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 cl...
| Publicado en: | Nonprofit & Voluntary Sector Quarterly Vol. 50; no. 3; pp. 662 - 688 |
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| Formato: | Artículo |
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Sage Publications Inc.
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=150066080&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 150066080 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 08997640 3FJ jtl: Nonprofit & Voluntary Sector Quarterly issn: 08997640 maglogo: Y pubinfo: dt: Jun2021 vid: 50 iid: 3 pid: 344 pub: Sage Publications Inc. artinfo: ui: 150066080 10.1177/0899764020968153 ppf: 662 ppct: 26 formats: tig: atl: Automated Coding Using Machine Learning and Remapping the U.S. Nonprofit Sector: A Guide and Benchmark. aug: au: Ma, Ji affil: The University of Texas at Austin, USA su: Nonprofit sector Nonprofit organizations Machine learning Source code Big data Data analysis sug: subj: Nonprofit sector Nonprofit organizations Other Social Advocacy Organizations Machine learning Source code Big data Data analysis keyword: BERT computational social science machine learning National Taxonomy of Exempt Entities neural network nonprofit organization BERT computational social science machine learning National Taxonomy of Exempt Entities neural network nonprofit organization ab: 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.80 to 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 more than 439,000 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. The working directory with all data sets, source codes, and historical versions are available on GitHub (https://github.com/ma-ji/npo_classifier). pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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