We are studying how different groups – including policymakers, farmers, scientists, and the public – think about genome editing and agriculture, to help guide future communication strategies.
We are evaluating how different stakeholders view genome editing in agriculture. We are using network science to construct “mental models,” or graphical representations of knowledge networks, of genome editing for four stakeholder groups: the non-expert public, biologists, policymakers, and farmers. Network science examines the relations between components in a system. In general, a system is represented graphically by points or objects, which signify the components, and lines or arrows that denote the relations among components.
In this study, the components are the words or phrases that comprise an individual's’ mental image of genome engineering. We will collect data using multiple, overlapping approaches, including focus groups, in-depth interviews, surveys, and concept mapping workshops. These data will allow us to empirically construct representations of how each group conceptualizes genome editing in agriculture. This information will guide the scientific community’s engagement strategies by identifying potential points of overlap and disparity with each group.
If you are interested in contributing to our study, please fill out an informational form found here and we will contact you by summer 2018.
Read "Online representations of “genome editing” uncover opportunities for encouraging engagement: A semantic network analysis" here.
Here is a brief summary of our findings:
Genome editing technologies are an emerging socio-scientific issue. As Americans are increasingly seeking science information online, it is important to determine how information about genome editing is portrayed online. We conducted a semantic network analysis, a type of content analysis that characterizes related concepts in text and develops a representative network through the analysis of concept associations, on the most prominent online information sources, Wikipedia and Google. We found that genome editing is represented on Wikipedia in largely technical terms, as supported by frames, including methodology/terminology, applications, common approaches, and DNA repair mechanisms. Similar results were identified for the Google webpages, with frames related to scientific contributions, applications, and methodology/terminology.
Results suggest that technical science terminology and an overall neutral sentiment dominate these online representations, indicating that there is a lack of hype around these sources. Our findings suggest that there is ample opportunity for the scientific community to promote public discourse around the benefits and pitfalls of genome-editing technologies, as these aspects are largely overlooked in online sources of information. These efforts may aid in bridging the gap between public stakeholders and scientists.
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