The Intersections between Sociology and STS: A Big Data Approach.

Area of Science:

• Sociology
• Science and Technology Studies (STS)
• Computational Social Science

Background:

• The intersection of sociology and STS is dynamic and warrants in-depth analysis.
• Academic publishing reflects broader political-economic and epistemic shifts within STS.
• Traditional methods may not fully capture complex interdisciplinary changes.

Purpose of the Study:

• To chart the changing intersections between sociology and STS.
• To characterize a "quali-quantitative" Big Data approach for social science research.
• To analyze epistemic and political-economic changes in STS academic publishing.

Main Methods:

• Employed computational textual analysis, a Big Data method, to analyze three decades of STS journals (1990-2019).
• Utilized IBM SPSS Modeler, a commercial analytics tool, for data mining.
• Complemented quantitative findings with qualitative data from 76 interviews with STS scholars.

Main Results:

• Identified specific intersections and shifts between sociology and STS through textual analysis.
• Demonstrated the utility of a Big Data approach in analyzing academic fields.
• Revealed significant political-economic and epistemic changes within STS.

Conclusions:

• Computational textual analysis offers a powerful "quali-quantitative" lens for understanding academic fields.
• The study highlights the increasing privatization of data and analytics tools in research.
• Findings contribute to understanding the evolution of STS and its relationship with sociology.