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 by the creators.
4.4 Positionality and interpretation
The authors of this work emphasize that the analysis, particularly the
subjective interpretation of the LDA results and the development of
stories, reflects individual positionality (i.e., Western,
non-Indigenous), perspective (i.e., external to the Arctic), and social
privilege (i.e., scientists from a research university). Awareness of
this is critical to appropriately situate and contextualize the
scenarios themselves, both as readers and writers. A story-based
scenario process is inevitably subjective, which is in fact part of the
motivation, given that stories can be an accessible format for engaging
broad audiences on complex topics. Nonetheless, we clarify our own
positionality above to provide additional context for those engaging
with these stories.
4.5 Future work
This work is intended to be a starting point for those interested in
expanding the scope of Arctic scenarios, by blending computational
methods with imaginative, story-based approaches. While the present
analysis is limited to two authors who are not currently located in the
Arctic, we hope that this pilot demonstration can serve as a launchpad
for engaging new partners. Specifically, we hope that communities that
are local and Indigenous to the Arctic will find the scenarios engaging
and explore the story-based methodology to develop future scenarios
tailored to their own communities.
A next step of this work could be to identify communities in the Arctic
that might specifically be a good fit for this type of scenario process.
Potential partners could include colleges and universities, particularly
those with a focus on educating local populations, for example Iḷisaġvik
College located in Utqiagvik, Alaska. Such a partnership could include a
combination of scenario-based learning, as well as collaborative
research, both among students and faculty. Eventually, if such a
scenario approach were successful in an Arctic academic context, a
broader effort could be made to work with communities beyond academia,
including tribal organizations and local governments. Many other
examples of structured futuring exist in Arctic contexts including with
Indigenous communities (e.g., Falardeau et al., 2019), though few
explicitly link the methods we discuss in this article (specifically
computational text analysis and story-based futures). All efforts in the
Arctic, however, are characterized by a need for deep engagement with
local communities, which require time, communication, and honest
partnership.
Some complexities exist for this work to be relevant for Arctic
communities, not least the technical barriers that exist for collecting
relevant texts, developing multi-lingual analytical capacity, the
provision of computing hardware, as well as technical training for
personnel. At the same time the entire method presented here does not
need to be adopted in its entirety. It is conceivable that a user of
this work could take our LDA analysis, and use it as a jumping off
point, such that other scenarios could be created following our
story-based approach yet based on the same LDA output. This would
sidestep the barriers to computational text analysis and would permit
new interpretation of the LDA results in a way that reflected the
perspective of the new users.
There are numerous possibilities for future expansion of the
computational text analysis. First, exploration of non-English corpuses
is a frontier for this work, not least because of the linguistic
diversity present in the Arctic. While it is well beyond the scope of
this analysis, collaborative research teams, possibly leveraging
circumpolar research networks, could leverage considerable local
capacity. Groups such as the Inuit Circumpolar Council, International
Arctic Science Committee and others could serve as valuable partners for
such an effort. In a similar context, several types of corpuses could be
collaboratively constructed, that explore differences among distinct
aspects of Arctic identity. This could include Indigenous and
non-Indigenous identities, as well as other cultural, national, and
ethnic groupings. To that end, methods such as word or topic intrusion
could be explicitly co-developed with stakeholders or partners to
strengthen topic model analyses, with local, on-the-ground perspectives
and knowledge. Finally, additional approaches could be employed to
connect any resultant scenarios further back to the original corpus
composition. First, information about the topic distribution across
documents could help test whether the scenarios reflect the document
composition more directly. Second, bi- and tri-gram frequency across the
corpus could be used to examine whether and how the eventual scenarios
relate to the raw, unanalyzed corpus.
As with the non-English analysis discussed above, such a comparative
approach would require broad collaboration among diverse groups
throughout the Arctic region. A central concern of work that engages
Indigenous and local people in the collection and interpretation of
perspectives on the future, is that of appropriately situating this
knowledge. Likewise, it would be necessary to carefully evaluate the
biases of the research questions that are asked. Substantial effort
would be required to detail the context of perspectives on the future,
and likewise to understand the cultural dynamics from which these
perspectives originate (D’ignazio and Klein, 2020).
This hybrid method of blending computational text analysis with
story-based approaches is not intended to be stand-alone. Rather, it can
serve as a complement to existing scenario development practices,
including conventional geophysical and Earth system science projections
of Arctic change. Moreover, this method provides a broad perspective on
locally driven, place-based initiatives. In this way, the methods and
results presented in this article can serve as a platform for
interweaving regional and circumpolar themes into local scenario
efforts.
5 Conclusions
The future Arctic will be profoundly different from that observed in the
present. We contribute a new approach toward creating scenarios of the
future Arctic, by blending computational text analysis with structured
futuring. The topic modeling yielded a set of distinct thematic clusters
of keywords, which were directly employed in the creation of story-based
visions of the future Arctic. The stories that were created permit a
visit to ten different, textured, and vital visions of the future. While
the stories are interesting, the major contribution of this work is to
demonstrate a method of how topic modeling can be used directly in an
imaginative scenario process. In the future, we anticipate that
computational text analysis could be incorporated as a component of
general scenario methods, to simultaneously provide an unsupervised scan
of existing literature, as well as to provide orthogonal insight that
might not be present in the existing worldview of the scenario creators.
Similarly, we anticipate that creative story-based methods will become
increasingly important for making sense of a world experiencing
accelerating and surprising change.
Acknowledgments, Samples, and Data
The authors thank Dr. Nathaniel Barnes and Nicholas Barnes for feedback
on the scenario stories, and Fabio Comin for the creation of the
artwork. The authors acknowledge no conflicts of interest. Metadata for
the articles that were downloaded and used in the corpus is available in
the Supplementary Materials of this article, entitled Table S1: Visions
of the Arctic Future _ Metadata for Arctic news corpus (Keys and Meyer,
2021).
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