Inuit Nunaat as an emerging region in area studies : building an Arctic studies program south of the tree line
Bibliographic record
Abstract
This dissertation addresses the emergence of the Arctic as a distinct world region and actor in international geopolitics and what this means for the field of area studies. I ask how the Arctic fits into the established field of area studies and how the unique characteristics of the region – as defined by Arctic Indigenous peoples – challenge Western understandings of what constitutes a global region including how we understand territory, sovereignty, and the relationship between space and social justice. To answer this question I analyze how the Arctic region maps onto the preexisting geographies of sovereignty as held by the U.S. Title VI program. In the United States the field of area studies has been significantly influenced by the Department of Defense and later the Department of Education via the Title VI grant program. Title VI provides grants to support area and international studies and foreign languages at colleges and universities across the country. The program has traditionally defined world regions based on the nation-state model, and it identifies important areas of the world as those critical to U.S. interests. In order to answer how the particular characteristics of the Arctic, specifically Indigenous worldviews, challenge and broaden current understandings of area studies I first seek to understand the Arctic from a northern perspective. How do the Inuit in Canada and internationally define their homeland, and what is the relevance of Inuit Nunangat (Inuit territory in Canada) and Inuit Nunaat (Inuit homeland internationally) to domestic and international relations? Next, I explore how Inuit concepts of territory further the voice and self-determination of the Inuit. Finally, I conduct an analytic autoethnography of the Arctic studies initiative at the University of Washington culminating in the inclusion of the Arctic as a distinct world region in the Canadian Studies Center's 2014 Title VI grant proposal. I argue that understanding the Arctic as a global actor – via the lens of new thinking in international relations theory, theories of social justice, and Inuit concepts of space – has the potential to reconfigure area studies in higher education to more effectively address 21st century global challenges.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".