Theoretical perspective reveals unexpected convergences between machine learning and grounded theory, suggesting new opportunities to construct robust hybrid qualitative-computational frameworks.
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.
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Müller et al. (2016) studied this question.
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