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Article
Theory identity: A machine-learning approach
Hawaii International Conference on System Sciences (HICSS)
  • Kai R. Larsen, University of Colorado at Boulder
  • Dirk Hovorka, Bond University
  • Jevin West, University of Washington
  • 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
Date of this Version
1-6-2014
Document Type
Conference Proceeding
Publication Details

Published version

Larsen, K. R., Hovorka, D., West, J., Birt, J., Pfaff, J. R., Chambers, T. W., Sampedro, Z. R., Zager, N., & Vanstone, B. (2014). Theory identity: A machine-learning approach. Paper presented at the Forty-Seventh Annual Hawaii International Conference on System Sciences (HICSS). 6-9 January, 2014. Hawaii, USA.

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Copyright © 2014 IEEE

2014 HERDC submission

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.

Citation Information
Kai R. Larsen, Dirk Hovorka, Jevin West, James Birt, et al.. "Theory identity: A machine-learning approach" Hawaii International Conference on System Sciences (HICSS) Vol. 47 (2014) p. 4639 - 4648 ISSN: 1530-1605
Available at: http://works.bepress.com/bruce_vanstone/47/