Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research

Abstract

Text-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 exploit state-of-the-art resources which digital era holds for text analysis. This paper illustrates the potential of deep-narrative analysis in energy policy research using text analysis tools from the cutting-edge domain of computational social sciences, notably topic modelling. We argue that a nested application of topic modelling and grounded theory in narrative analysis promises advances in areas where manual-coding driven narrative analysis has traditionally struggled with directionality biases, scaling, systematisation and repeatability. The nested application of the topic model and the grounded theory goes beyond the frequentist approach of narrative analysis and introduces insight generation capabilities based on the probability distribution of words and topics in a text corpus. In this manner, our proposed methodology deconstructs the corpus and enables the analyst to answer research questions based on the foundational element of the text data structure. We verify theoretical compatibility through a meta-analysis of a state-of-the-art bibliographic database on energy policy, narratives and computational social science. Furthermore, we establish a proof-of-concept using a narrative-based case study on energy externalities in slum rehabilitation housing in Mumbai, India. We find that the nested application contributes to the literature gap on the need for multidisciplinary methodologies that can systematically include qualitative evidence into policymaking.

India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling

“…The objective of TM is to extract latent semantic topics from large volumes of textual documents (i.e., corpora). LDA is a widely used unsupervised TM technique, with recent applications spanning across political science and rhetoric analysis [6-8, 14, 15], disaster management [10,16,17] and public policy [11,12,18]. It is a generative probabilistic method for modelling a corpus that assigns topics to documents and generates distributions over words given a collection of texts.…”

“…More recent applications of TM includes crisis identification in urban areas for evidencebased policymaking [10], deep narrative analysis for deriving intervention points for distributive energy justice in poverty [11] and informed public policy design in public administration [12]. However, none of the above applications of TM had explored the policy reactions of a government towards handling a national emergency using publicly available dataset.…”

Analysis of Renewable Energy Policies through Decision Trees

“…No renewable energy target and policy achievement analysis has been found to compare its results against the ones presented in this work. An ex-ante methodology for policy impact assessment is proposed to the European Commission [30], serving as a guideline for the member states supporting the objective achievement of climate change policies (energy policies included). It proposes comparing GHG levels ex-ante and ex-post of the implementation of the policies.…”

Unpacking Invisible Work Practices, Constraints, and Latent Power Relationships in Child Welfare through Casenote Analysis

“…Our survey of prior literature shows that while much of SIGCHI research has indirectly examined sociotechnical systems in the public sector, there is a dearth of SIGCHI research that employs computational text analysis to examine these complex systems. And yet, outside of the SIGCHI community, scholars have actively examined the utility of applying computational text analysis methods (specifically topic modeling) to sociotechnical systems research in the public sector [45,63,74,101] and have noted that topic modeling methods can aid qualitative methods by guiding the systematic discovery of information [74] and help reduce directionality bias that arises from manual interpretations of text [45]. Therefore, responding to these calls by SIGCHI scholars, we employed topic modeling techniques for analyzing child-welfare casenotes.…”