ABSTRACT
The expression "public opinion" has long been part of common parlance. However, its value as a scientific measure has been the topic of abundant academic debates over the past several decades. Such debates have produced more variety and contestations rather than consensus on the very definition of public opinion, let alone on how to measure it. This study reports on the usefulness of web-based big data digital network analytics in deciphering the distributed meanings and sense making related to controversial biotechnology applications. Using stem cell therapies as a case study, we argue that such digital network analysis can complement the traditional opinion polls while avoiding the sampling bias that is typical of opinion polls. Although the polls cannot account for the opinion dynamics, combining them with web-based big data analysis can shed light on three dimensions of public opinion essential for sense making: counts or volume of opinion data, content, and movement of opinions. This approach is particularly promising in the case of ongoing scientific controversies that increasingly overflow into the public sphere morphing into public political debates. In particular, our study focuses as a case study on public controversies over the clinical provision of stem cell therapies. Using web entities specifically addressing stem cell issues, including their dynamic aggregation, the internal architecture of the web corpus we report in this study brings the third dimension of public opinion (movement) into sharper focus. Notably, the corpus of stem cell networks through web connectivity presents hot spots of distributed meaning. Large-scale surveys conducted on these issues, such as the Eurobarometer of Biotechnology, reveal that European citizens only accept research on stem cells if they are highly regulated, while the stem cell digital network analysis presented in this study suggests that distributed meaning is promise centeredness. Although major scientific journals and companies tend to structure public opinion networks, our finding of promise centeredness as a key ingredient of distributed meaning and sense making is consistent with therapeutic tourism that remains as an important facet of the stem cell community despite the lack of material standards. This new approach to digital network analysis has crosscutting corollaries for rethinking the notion of public opinion, be it in electoral preferences or as we discuss in this study, for new ways to measure, monitor, and democratically govern emerging technologies.
Subject(s)
Big Data , Biotechnology , Data Mining , Internet , Public Opinion , Biological Science Disciplines , Data Analysis , Data Mining/methods , Humans , Stem Cells/metabolism , Surveys and QuestionnairesABSTRACT
La comprensión adecuada de la bioética global requiere entender el callejón sin salida del actual paradigma de civilización, anclado al despliegue ilimitado del paradigma baconiano, a fin de superar la crisis presente de civilización. Por tanto, este artículo procura mostrar la posibilidad de tal comprensión por la vía del concepto de energía como leitmotiv clave a fin de purgar la bioética global de contaminaciones ideológicas indeseables.
The adequate comprehension of the global bioethics requires understanding the blind alley of the present-day paradigm of civilization, which is anchored to the unlimited unfolding of Francis Bacon's paradigm, so as to overcome the current crisis of our civilization. Therefore, this article tries to demonstrate the possibility of such a comprehension by means of the concept of energy as a key fundamental theme in order to purge the global bioethics of undesirable ideological corruptions.
A compreensão adequada da bioética global requer entender o ponto morto do atual paradigma de civilização, atado ao credo baconiano ilimitado, a fim de superar a crise da nossa civilização. Portanto, este artigo tenta mostrar a possibilidade de tal entendimento por meio do conceito de energia como tema fundamental para eliminar da bioética global a poluição ideológica indesejável.
