Topic Modeling the Research‐Practice Gap in Public Administration

Walker, Richard M.; Chandra, Yanto; Zhang, Jiasheng; Witteloostuijn, A. van

doi:10.1111/puar.13095

Cited by 46 publications

(46 citation statements)

References 20 publications

(25 reference statements)

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“…These assumptions are coherent with the arguments of Trotter, (2012) and Bryant & Charmaz, (2019). Moreover, from a pure applied public policy perspective, Walker et al, (2019) have argued the need for objective and robust qualitative approaches in evidence-based policymaking. They also point out that the current qualitative methods have a strong directionality quotient (referred to as 'hegemonic interpretation', pp 475) that is sensitive to the interpretabilitybias of the researcher, analyst or policymaker.…”

Section: Conceptual Framework Of the Proposed Nested Deep-narrative Asupporting

confidence: 69%

“…The authors also established that topic modelling approach in energy research could provide a new outlook to evidence-based policymaking. More recently, Walker, Chandra, Zhang, & van Witteloostuijn, (2019) have verified the critical importance of topic modelling-based approaches in deriving evidence for policy design and policymaking in public administration.…”

Section: Topic Modelling In Humanities and Social Science Researchmentioning

confidence: 87%

“…It is expected to enhance the insight generation capability of topic modelling in policy research. Walker et al, (2019) argues that the absence of this insight generating capability in the current methods of topic modelling in policy research is a structural limitation.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

A nested computational social science approach for deep-narrative analysis in energy policy research

Debnath¹,

Darby²,

Bardhan³

et al. 2020

Preprint

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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 the theoretical and epistemological fit of the proposed nested methodology through a meta-analysis of a state-of-the-art bibliographic database on energy policy and computational social science. We find that the nested application contributes to the literature gap on the need for multidisciplinary polyvalence methodologies that can systematically include qualitative evidence into policymaking.

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Section: Conceptual Framework Of the Proposed Nested Deep-narrative Asupporting

confidence: 69%

Section: Topic Modelling In Humanities and Social Science Researchmentioning

confidence: 87%

See 1 more Smart Citation

A nested computational social science approach for deep-narrative analysis in energy policy research

Debnath¹,

Darby²,

Bardhan³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The authors also established that topic modelling approach in energy research could provide a new outlook to evidence-based policymaking. More recently, Walker, Chandra, Zhang, & van Witteloostuijn [46] have verified the critical importance of topic modelling-based approaches in deriving evidence for policy design and policymaking in public administration.…”

Section: Introductionmentioning

confidence: 96%

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

Debnath

Darby

Bardhan

et al. 2020

Energy Research & Social Science

View full text Add to dashboard Cite

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.

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“…As a point of discussion, Oliver () proposes an alternative to the political economy of behavioral public policy, suggesting that policy makers provide an environment that is conducive to each person's own conception of a flourishing life while at the same time regulating against behaviorally informed harms and for behaviorally induced, otherwise forgone, benefits. Somewhat unrelated, but quite significant, is Walker et al's () discussion of the research–practice gap. Using topic modeling, the authors offer highlights for how the two groups can shed more light on topics of mutual and distinct interests.…”

mentioning

confidence: 99%