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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