Doctoral Dissertation Research: Unveiling Conceptual Shifts and Novel Dynamics in Genetic Engineering Science: A Gene Drive Case Study Doctoral Dissertation Research: Unveiling Conceptual Shifts and Novel Dynamics in Genetic Engineering Science: A Gene Drive Case Study How is knowledge created at the intersections between basic science, biotechnology, and industry? Gene drives are an interesting example, as they combine a long-standing theoretical interest with a recent technological breakthrough and a new set of practical and commercial applications. Gene drives are genes engineered such that they are preferentially inherited at a frequency greater than the typical Mendelian fifty percent. During the historical and conceptual evolution of gene drives beginning in the 1960s, there has been a large number of innovations and publications. Along with that, gene drive science developed considerable public attention, novel connective relationships, and linguistic variation. It is now time to look at this new organization of science using a systematic approach to characterize the structure and dynamics of the gene drive knowledge system. During this training project Cody OToole will ask: (1) How is the term gene drive utilized by scientists, institutions, corporations, and social media? (2) How have those actors shaped the overall scientific knowledge system? (3) What are the factors that contribute to the diverse public opinions seen in genetic engineering? (4) How similar is the gene drive technologys progress to other scientific fields? What influence has the gene drive discourse had on CRISPR-Cas9 science? (5) To what extent can the gene drives conceptual development be used to predict the trajectory of the CRISPR-Cas9 science knowledge system? What are the contextual factors that influence the evolution of scientific knowledge? The research takes place within the Laubichler lab at Arizona State University and draws multiple interdisciplinary methodologies led by Manfred Laubichler. These include: traditional historical research, computational history of science, computational social science, data science, complexity science, and network science. Depicting the structure, dynamics, and evolution of scientific knowledge during a novel eruption of scientific complexity can shed light on the factors that can lead to: (1) improved scientific communication, (2) reduction of scientific progress, (3) new knowledge, and (4) novel collaborative relationships. Therefore characterizing the current technological, methodological, and social contexts that can influence scientific knowledge.
|Effective start/end date||9/15/21 → 8/31/22|
- National Science Foundation (NSF): $15,129.00
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