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Presentation
Theory identity: A machine-learning approach (in press)
47th Hawaii Conference of System Sciences (2014)
  • Kai R Stirling, University of Colorado at Boulder
  • Dirk Hovorka, Bond University
  • Jevin West
  • James Birt, Bond University
  • James R Pfaff, University of Colorado at Boulder
  • Trevor W Chambers, University of Colorado at Boulder
  • Zebula R Sampedro, University of Colorado at Boulder
  • Nick Zager, University of Colorado at Boulder
  • Bruce Vanstone, Bond University
Abstract
Theory identity is a fundamental problem for researchers seeking to determine theory quality, create theory ontologies and taxonomies, or perform focused theory-specific reviews and meta-analyses. We demonstrate a novel machine-learning approach to theory identification based on citation data and article features. The multi-disciplinary ecosystem of articles which cite a theory’s originating paper is created and refined into the network of papers predicted to contribute to, and thus identify, a specific theory. We provide a ‘proof-of-concept’ for a highly-cited theory. Implications for crossdisciplinary theory integration and the identification of theories for a rapidly expanding scientific literature are discussed.
Keywords
  • theory identity,
  • theory ontologies
Publication Date
January 6, 2014
Comments
Citation only

Larsen, K.R., Hovorka, D., West, J., Birt, J., Pfaff, J.R., Zager, N., & Vanstone, B. (in press). Theory identity: A machine-learning approach. Paper to be presented at the 47th Hawaii Conference of System Sciences, 6-9 January, Big Island, Hawaii

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Citation Information
Kai R Stirling, Dirk Hovorka, Jevin West, James Birt, et al.. "Theory identity: A machine-learning approach (in press)" 47th Hawaii Conference of System Sciences (2014)
Available at: http://works.bepress.com/james_birt/11/