Exploring public discourses about emerging biotechnologies through automated coding of open-ended survey questions

The primary method by which social scientists describe public opinion about science and technology is to present frequencies in quantitative variables and to use multivariate statistical models to predict where different groups stand on issues of scientific controversy. Such an approach requires measures of individual preference which can be aligned numerically in an ordinal or, preferably, a continuous manner along an underlying evaluative dimension – generally the standard 5 or 7-point survey attitude item. The key concern motivating the present paper is whether, due to the low salience and ‘difficult’ nature of much biomedical science for members of the general public, it is sensible to require respondents to choose from amongst a small and predefined set of evaluative response categories. Here, we adopt a different methodological approach; the analysis of textual responses to ‘open-ended’ questions, in which respondents are asked to state, in their own words, what they understand by terms such as 'stem cell' and 'DNA'. To this textual data we apply the statistical procedures encoded in the Alceste software package to detect and classify underlying discourse and narrative structures. We then examine the extent to which the classifications, thus derived, can aid our understanding of how the public develop and use ‘everyday’ images of, and talk about, biomedicine to structure their evaluations of moral acceptability, risk, and harm of emerging technologies.