Studying Culture and Meaning Through Interpretative Computational Methods: From theory to method and back
Jan Goldenstein et al.
Abstract
The convergence of expanded data availability, technological breakthroughs, and evolving societal dynamics has rekindled scholarly interest in culture and meaning. Accompanying computational methods are advancing rapidly across natural language processing, digital image processing, machine learning, neural networks, and artificial intelligence, which creates unprecedented opportunities for cultural meaning analysis. The introduction to this special issue advances abductive theorizing and reflexive rendering to explore the dynamic interplay of theory generation, measurement, and interpretation, highlighting common themes across its five articles. Our analysis unfolds in three stages. We begin with an abductive examination of the culture and meaning theories covered in the articles, exploring how they shape data selection and preparation, as well as research designs for addressing theoretical questions. Next, we engage in reflexive rendering, scrutinizing data characteristics and representations, methodological approaches, computational methods, and the theoretical artifacts that emerge to advance theory development. Finally, we discuss possible contributions, challenges, and concerns when organizational research examines culture and meaning using computational methods.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.