Theory identity: A machine-learning approach
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Electronic versions
DOI
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 language | English |
|---|---|
| Title of host publication | Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014 |
| Place of Publication | United States |
| Publisher | IEEE Computer Society Press |
| Pages | 4639-4648 |
| Number of pages | 10 |
| ISBN (print) | 9781479925049 |
| DOIs | |
| Publication status | Published - 2014 |
| Externally published | Yes |