Theory identity: A machine-learning approach

Kai R. Larsen, Dirk Hovorka, Jevin West, James Birt, James R. Pfaff, Trevor W. Chambers, Zebula R. Sampedro, Nick Zager, Bruce Vanstone

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

4 Citations (Scopus)

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 cross-disciplinary theory integration and the identification of theories for a rapidly expanding scientific literature are discussed.

Original languageEnglish
Title of host publicationProceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014
PublisherIEEE Computer Society
Pages4639-4648
Number of pages10
ISBN (Print)9781479925049
DOIs
Publication statusPublished - 2014
Event47th Hawaii International Conference on System Sciences, HICSS 2014 - Waikoloa, Waikoloa, HI, United States
Duration: 6 Jan 20149 Jan 2014

Conference

Conference47th Hawaii International Conference on System Sciences, HICSS 2014
CountryUnited States
CityWaikoloa, HI
Period6/01/149/01/14

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  • Cite this

    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. In Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014 (pp. 4639-4648). [6759171] IEEE Computer Society. https://doi.org/10.1109/HICSS.2014.564