Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination

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dc.contributor.authorMuller, Michael
dc.contributor.authorGuha, Shion
dc.contributor.authorBaumer, Eric P.S.
dc.contributor.authorMimno, David
dc.contributor.authorShami, N. Sadat
dc.date.accessioned2023-03-17T22:48:30Z
dc.date.available2023-03-17T22:48:30Z
dc.date.issued2016
dc.description.abstractGrounded 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.doi10.1145/2957276.2957280
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4438
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofProceedings of the 2016 ACM International Conference on Supporting Group Work
dc.subjectsupervised learning
dc.subjectcoding families
dc.subjectmachine learning
dc.subjectaxial coding
dc.subjectunsupervised learning
dc.subjectgrounded theory
dc.titleMachine Learning and Grounded Theory Method: Convergence, Divergence, and Combinationen
dc.typeText/Conference Paper
gi.citation.startPage3–8
gi.conference.locationSanibel Island, Florida, USA

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