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dc.contributor.authorDebnath, R.
dc.contributor.authorDarby, S.
dc.contributor.authorBardhan, R.
dc.contributor.authorMohaddes, K.
dc.contributor.authorSunikka-Blank, M.
dc.date.accessioned2020-12-04T16:25:10Z
dc.date.available2020-12-04T16:25:10Z
dc.date.issued2020-07-14
dc.identifier.otherCWPE2062
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/314715
dc.description.abstractText-based data sources like narratives and stories have become increasingly popular as critical insight generator in energy research and social science. However, their implications in policy application usually remain superficial and fail to fully exploi
dc.publisherFaculty of Economics, University of Cambridge
dc.relation.ispartofseriesCambridge Working Papers in Economics
dc.rightsAll Rights Reserved
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.subjectenergy policy
dc.subjectnarratives
dc.subjecttopic modelling
dc.subjectcomputational social science
dc.subjecttext analysis
dc.subjectmethodological framework
dc.titleGrounded reality meets machine learning: A deep-narrative analysis framework for energy policy research
dc.typeWorking Paper
dc.identifier.doi10.17863/CAM.61821


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