Abstract
The importance of expectations for the future is well recognised within the social sciences. Future scenarios as well as expectations are relevant components of emerging technoscientific innovations to be investigated. The investigation of expectations has been translated into a useful perspective for the sociological gaze seeking insights about innovation processes. In general, a relevant data source about discourses contributing to the construction of innovation networks is the discourse brought up by mass media. Therefore, we selected the media as our area of research for investigating socio-technical future scenarios. This presents several research challenges linked to content analysis and trend analysis. We selected three of them, namely: the detection of future scenarios in the media; wherever different representations of future scenarios do exist, whether they are complementary or competing; and whether and how representations of futures outlined in the past can mobilise actors in the present. We addressed these challenges by proposing a combination of research techniques ranging from text mining to social network analysis.
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Notes
“Techno-science” is the expression usually used by Science and Technology Studies scholars to stress that science and technology cannot be considered as separate both because research activities are inextricably interwoven with technological devices and all technological artefacts commonly used in everyday life are nothing but materialised scientific knowledge (Shapin 2008; Latour 1987).
The TIPS project developed an automatic text classifier that allows to discriminate technoscientific articles on the basis of content.
See supplementary materials for details.
As a training exercise, Gephi’s tutorial offers the dataset of Victor Hugo’s Les Miserables characters’ relationships (www.youtube.com/watch?v=371n3Ye9vVo, last accessed 7 April 2019). Even though in developing the TRE approach we relied theoretically on scientific references, we have been inspired by the training exercise as well.
The density is calculated by relating the number of actual edges in the graph to the maximum possible (Wasserman and Faust 1994, p. 101)
The WD is obtained by weighing the number of edges for each node of the network by the intensity of each connection. This equals to 25% of the actors of each network.
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Neresini, F., Giardullo, P., Di Buccio, E. et al. Exploring socio-technical future scenarios in the media: the energy transition case in Italian daily newspapers. Qual Quant 54, 147–168 (2020). https://doi.org/10.1007/s11135-019-00947-w
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DOI: https://doi.org/10.1007/s11135-019-00947-w