Collaborating with the Machines: A Hybrid Method for Classifying Policy Documents.
Governments produce vast and growing quantities of freely available text: laws, rules, budgets, press releases, and so forth. This information flood is facilitating important, growing research programs in policy and public administration. However, tightening research budgets and the information's va...
| Publicado en: | Policy Studies Journal Vol. 48; no. 1; pp. 184 - 207 |
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| Autores principales: | , |
| Formato: | Artículo |
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Wiley-Blackwell
Feb2020
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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=141783288&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 141783288 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0190292X PSJ jtl: Policy Studies Journal issn: 0190292X maglogo: Y pubinfo: dt: Feb2020 vid: 48 iid: 1 pid: 480 pub: Wiley-Blackwell artinfo: ui: 141783288 10.1111/psj.12245 ppf: 184 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 600KB tig: atl: Collaborating with the Machines: A Hybrid Method for Classifying Policy Documents. aug: au: Loftis, Matt W. Mortensen, Peter B. su: Political science research Human-machine relationship Research methodology Machine learning Classification sug: subj: Political science research Human-machine relationship Research methodology Machine learning Classification keyword: classifying policy documents machine coding policy agendas classifying policy documents machine coding policy agendas ab: Governments produce vast and growing quantities of freely available text: laws, rules, budgets, press releases, and so forth. This information flood is facilitating important, growing research programs in policy and public administration. However, tightening research budgets and the information's vast scale forces political science and public policy to aspire to do more with less. Meeting this challenge means applied researchers must innovate. This article makes two contributions for practical text coding—the process of sorting government text into researcher‐defined coding schemes. First, we propose a method of combining human coding with automated computer classification for large data sets. Second, we present a well‐known algorithm for automated text classification, the Naïve Bayes classifier, and provide software for working with it. We argue and provide evidence that this method can help applied researchers using human coders to get more from their research budgets, and we demonstrate the method using classical examples from the study of policy agendas. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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