Transparency to Visibility (T2V): Network Visualization in Humanities Research
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Bioethicists and humanistic researchers alike have long been concerned over the effects of unchecked industry money on biomedical cultures. Corporate-funded clinical trials, free lunches, free travel, and industry honoraria for scientists have been shown to adversely affect the integrity of biomedical research. Despite the broad recognition of these hazards, efforts to address them have largely been limited to disclosure requirements and related training. Current standards require clinical researchers to report certain conflicts of interest alongside published scholarship. Unfortunately, a growing body of evidence indicates that disclosure statements often result in unintended and pernicious effects. For example, they have been shown to cause audiences to extend more trust to those holding conflicts of interest, as disclosure provides an opportunity to display both honesty and expertise. Conflict disclosure can also lead to "moral licensing," a phenomenon whereby those who disclose conflicts become unduly confident in their objectivity because transparency obligations have been fulfilled. Finally, disclosure requirements embody some of the most concerning aspects of neoliberal oversight in that they focus attention on individual behavior rather than cultural conditions. Ultimately, a significant shift in our understanding of conflicts of interest is required, and this is where the insights of digital humanities (DH) can be most beneficial. However, in order for this kind of theoretically-informed relational network modeling to be of use to humanities research, scholars need effective and efficient tools to transform the dense prose of disclosure statements, financial reports, or database outputs into useable visualizations. This is precisely what the Transparency to Visualization (T2V) tool is designed to do. One of the central intellectual projects in the humanities over the past several decades has been to develop robust theoretical accounts of power and influence within relational assemblages. Extending this project technologically, DH researchers have developed a robust and rapidly growing set of network analysis and visualization tools. Indeed, in recent years DH researchers have leveraged network visualization algorithms to create impressive accounts of citation structures, social media networks, archival letters, and so on. However, despite the impressive proliferation of network visualization technologies, preparing humanistic data for network visualization can be a significant challenge. While some data sets, like social media repositories, have the kind of rich structured metadata that can support rapid visualization, creating networks from more traditional textual data sets can present significant challenges, especially at scale. The T2V project team, which includes scholars in DH, rhetoric, and technical communication, is working to address these challenges through the development of a DH tool that is capable of extracting relational data out of prose. Our toolkit combines natural language processing, machine-learning enhanced named-entity recognition, and regular expressions to create a script that "reads" biomedical disclosure statements for economic relationships and prepares extracted data for subsequent visualization. The proposed presentation will explain and demonstrate the toolkit while reflecting on how these kinds of resistant DH projects can contribute positively to issues of social concern.
DescriptionDigital Frontiers Session: Rhetoric and Resistance
Annotated PowerPoint (.pptx) available for download.