Debnath Ramit, Darby Sarah, Bardhan Ronita, Mohaddes Kamiar, Sunikka-Blank Minna
Behaviour and Building Performance Group, The Martin Centre for Architectural and Urban Studies, Department of Architecture, University of Cambridge, Cambridge CB2 1PX, United Kingdom.
Energy Policy Research Group, Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom.
Energy Res Soc Sci. 2020 Nov;69:101704. doi: 10.1016/j.erss.2020.101704.
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.
诸如叙述和故事等基于文本的数据源，作为能源研究和社会科学中关键的见解生成方式，已变得越来越流行。然而，它们在政策应用中的影响通常仍较为表面，未能充分利用数字时代为文本分析所提供的前沿资源。本文运用计算社会科学前沿领域的文本分析工具，特别是主题建模，阐述了深度叙事分析在能源政策研究中的潜力。我们认为，在叙事分析中嵌套应用主题建模和扎根理论，有望在传统上手动编码驱动的叙事分析在方向性偏差、规模扩展、系统化和可重复性方面面临困境的领域取得进展。主题模型和扎根理论的嵌套应用超越了叙事分析的频率主义方法，并基于文本语料库中单词和主题的概率分布引入了见解生成能力。通过这种方式，我们提出的方法解构了语料库，并使分析人员能够基于文本数据结构的基础元素回答研究问题。我们通过对关于能源政策、叙事和计算社会科学的前沿文献数据库进行元分析，验证了理论兼容性。此外，我们利用印度孟买贫民窟改造住房中能源外部性的基于叙事的案例研究建立了一个概念验证。我们发现，这种嵌套应用弥补了文献空白，即需要能够将定性证据系统地纳入政策制定的多学科方法。
