Fig 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.
3.3 LDA topic quadrants
We labeled the axes of the principal component quadrants to define
overarching context for the scenarios, to ensure that topics which are
close to one another in the quadrant space are similar in some way,
while those far apart are dissimilar. To be clear, this step is entirely
user-defined, and relies on human interpretation of the text analysis.
For the purposes of our scenarios, which aim to explore thefuture of the Arctic, we use two broad features that would serve
as exogenous drivers for the entire region: climate change severity and
regional levels of cooperation.
The x-axis describes the relative severity of climate change in the
Arctic region, with the right side representing anticipated climate
changes, and the left side representing extreme climate changes. Given
the overwhelming evidence to date of how the Arctic is
disproportionately sensitive to climate change (Dodds, 2010; Post et
al., 2009; Steiner et al., 2019; Stroeve et al., 2012), the
concentration of most of the scenarios in that domain is supported
empirically. The left side of the x-axis represents more extreme climate
changes, which we acknowledge are possible in the range of climate
projections, as well as being exceptionally consequential to the
region.
The y-axis describes whether the Arctic region is characterized by
conflict or cooperation. The top of this axis represents high
cooperation, including economically, politically, socially, and
militarily. The bottom of this axis represents low cooperation. In this
way, the bulk of the thematic clusters are somewhat neutral, and this is
broadly reflected in the patchy history of Arctic cooperation (Osherenko
& Young, 2005). There have been periods of peace and international
cooperation, exemplified, for example, by the effectiveness of the
Arctic Council (Young, 2010). Likewise, there have been profound periods
of conflict (Keil, 2014; Rahbek-Clemmensen, 2017; Young, 2011), as well
as targeted violence toward Indigenous peoples (Crawford, 2014; Salusky
et al., 2021).
To be clear — the position of the topic clusters in this quadrant
space does not determine the content of the scenario but helps to frame
the context of the scenario. Thus, when the scenarios produced in
this analysis are compared with one another, those that were identified
computationally as more tightly clustered exist in similar types of
geopolitical and climatic contexts (i.e., topics 1, 2, 3, 4, 5, 6),
whereas those that were distributed throughout the intertopic space will
be more idiosyncratic (i.e., topics 7, 8, 9, 10).
3.4 Distribution of topic clusters in the Intertopic Distance Map
We can clearly interpret the current dominance of each topic relative to
the others, with topics 1-6 being quite dominant and topics 7-10 being
less so. This information provides additional context of how
representative a given theme is for the entire corpus. Here we provide a
summary showing each topic with its corresponding keywords (Table 1).