Figure 1 . Conceptual overview of how the topic modeling
analysis feeds into the structured futuring process, including
worldbuilding and story creation
Figure 2. Results of the latent Dirichlet allocation, with
thematic dominance and clustering visualized on the left, and the
baseline frequency of keywords across the entire corpus depicted on the
right. This was generated using the freely available pyLDAvis software
in the GENSIM Python package.
Figure 3. Summary of the story-based scenario creation
procedure for Topic #5 ‘Concession 60’. Steps begin at the top left,
and can be read left-to-right, and top-to-bottom.
Figure 4. Examples of visual art for two of the story-based
scenarios. The top panel is an image inspired by ‘Concession 60’ (i.e.,
Topic #5), and the bottom panel is an image inspired by ‘Assisted
Migration’ (i.e., Topic #9). Artwork is used with permission by Patrick
W. Keys and Fabio Comin. All rights reserved.
Table 1 . Summary of topic clusters with corresponding top 30
keywords in that cluster with terms listed in descending order of
relevance within the topic (i.e., the first term is the most salient to
the topic).
Table 2 . Overview of stories, including theme, title, and brief
description of the world and story.
Table 3 . Comparison of a sample of existing Arctic scenario
narratives with the ten scenarios produced in this analysis