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This viewpoint exemplifies how advances in computational text analysis allow for analysing novel data for transitions studies. Applying the specific technique of topic modelling to plenary talks related to energy in the Finnish parliament during 2008–2020 illustrates how the approach can be of use to transitions studies. In particular, it aids in exploring the dynamics of diverging perspectives, such as by right-wing populists and the greens, on outlooks for energy transitions. We sketch how computational text analysis can further contribute to transitions studies.

This article examines energy related experiments suggested and outlined in a co-creative Energy Transition Arena in Finland in 2017 with participating experts from various spheres of society. A transition arena is a process for engaging stakeholders in systemic transitions, and experiments are an important measure for enhancing transition and mechanisms of change. We apply network analysis and use the Theories of Change evaluation framework to reveal the implicit expectations and change theories of the arena participants. In the analysis, we first deconstruct the experiments into their key elements and then reconstruct them in the form of visual networks, which helps us to identify what the examined experiments have in common and how their elements connect. The data visualisations of the networks further help to examine how the arena participants construct transitions in the Finnish energy sector. peerReviewed