Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination
| dc.contributor.author | Muller, Michael | |
| dc.contributor.author | Guha, Shion | |
| dc.contributor.author | Baumer, Eric P.S. | |
| dc.contributor.author | Mimno, David | |
| dc.contributor.author | Shami, N. Sadat | |
| dc.date.accessioned | 2023-03-17T22:48:30Z | |
| dc.date.available | 2023-03-17T22:48:30Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | Grounded Theory Method (GTM) and Machine Learning (ML) are often considered to be quite different. In this note, we explore unexpected convergences between these methods. We propose new research directions that can further clarify the relationships between these methods, and that can use those relationships to strengthen our ability to describe our phenomena and develop stronger hybrid theories. | en |
| dc.identifier.doi | 10.1145/2957276.2957280 | |
| dc.identifier.uri | https://dl.eusset.eu/handle/20.500.12015/4438 | |
| dc.language.iso | en | |
| dc.publisher | Association for Computing Machinery | |
| dc.relation.ispartof | Proceedings of the 2016 ACM International Conference on Supporting Group Work | |
| dc.subject | supervised learning | |
| dc.subject | coding families | |
| dc.subject | machine learning | |
| dc.subject | axial coding | |
| dc.subject | unsupervised learning | |
| dc.subject | grounded theory | |
| dc.title | Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination | en |
| dc.type | Text/Conference Paper | |
| gi.citation.startPage | 3–8 | |
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| gi.conference.location | Sanibel Island, Florida, USA |