Abstract
Quantitative and qualitative studies of science have historically played radically different roles with opposing epistemological commitments. Using large-scale text analysis, we see that qualitative studies generate and value new theory, especially regarding the complex social and political contexts of scientific action, while quantitative approaches confirm existing theory and evaluate the performance of scientific institutions. Large-scale digital data and emerging computational methods could allow us to refigure these positions, turning qualitative artifacts into quantitative patterns into qualitative insights across many scales, heralding a new era of theory development, engagement, and relevance for scientists, policy-makers, and society.
Highlights
In Paul Feyerabend’s philosophical treatise Against Method (1975), he outlined an “anarchistic theory of knowledge” that argued all major scientific advances eschew any generalizable notion of scientific method
The divide between quantitative and qualitative studies of science is deeper, and we will argue this divide limits the potential for insight, advance and relevance that could come from greater collaboration and an explosion of ontological and epistemic commitments
For the prospect of more deeply understanding and engaging with science, we argue against method
Summary
In Paul Feyerabend’s philosophical treatise Against Method (1975), he outlined an “anarchistic theory of knowledge” that argued all major scientific advances eschew any generalizable notion of scientific method. We will perform a 21st-century replication of Snow’s cocktail party experiment on quantitative and qualitative students of science, and argue forcefully that a reformulated treaty between quantitative and qualitative studies could portend a renaissance of engagement with scientists about what they do—forward and defend supportable claims—at the level at which they do it. It could span scales of analysis, connecting scientific action to science policy and civic action, leading to new discoveries and new relevance. We detail how new data on science and computational methods open up new pathways for collaboration and mutual learning that could provoke advance in our understanding and engagement with science
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