Title

Theory identity: A machine-learning approach

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.

Access the conference

Copyright © 2014 IEEE

2014 HERDC submission

ISSN

1530-1605

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.

 

This document has been peer reviewed.