Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77314
Title: A corpus-based investigation of techno-optimism and propositional certainty in the National Intelligence Council’s ‘Future Global Trends Reports’ (2010-2035)
Authors: McKeown, J 
Keywords: Categorical certainties
Futurological discourse
Ideological bias
Objective epistemic modality
Possibilities
Probabilities
Relative certainties
Techno-optimism
Technology
Issue Date: 2018
Publisher: SAGE Publications
Source: Discourse and communication, 2018, v. 12, no. 1, p. 39-57 How to cite?
Journal: Discourse and communication 
Abstract: This article reports the findings from a study of discursive representations of the future role of technology in the work of the US National Intelligence Council (NIC). Specifically, it investigates the interplay of ‘techno-optimism’ (a form of ideological bias) and propositional certainty in the NIC’s ‘Future Global Trends Reports’. In doing so, it answers the following questions: To what extent was techno-optimism present in the discourse? What level of propositional certainty was expressed in the discourse? How did the discourse deal with the inherent uncertainty of the future? Overall, the discourse was pronouncedly techno-optimist in its stance towards the future role of technology: high-technological solutions were portrayed as solving a host of problems, despite the readily available presence of low-technology or no-technology solutions. In all, 75.1% of the representations were presented as future categorical certainties, meaning the future was predominantly presented as a known and closed inevitability. The discourse dealt with the inherent uncertainty of the subject matter, that is, the future, by projecting the past and present into the future. This was particularly the case in relation to the idea of technological military dominance as a guarantee of global peace, and the role of technology as an inevitable force free from societal censorship.
URI: http://hdl.handle.net/10397/77314
ISSN: 1750-4813
EISSN: 1750-4821
DOI: 10.1177/1750481317735625
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