The ambivalence of the implementation of the US arctic policy: integrating and disintegration factors of the allies
Bibliographic record
Abstract
The Arctic region is gaining increasing strategic importance due to its economic potential, resource richness, and shifting geopolitical landscape. The United States has recognized this significance and has established alliances and partnerships with various countries in the region to enhance its positions and interests. However, concerns exist regarding the limited understanding of the complex dynamics and evolving relationships among the US Arctic allies. The lack of comprehensive analysis and up-to-date information hinders the understanding of their strategic documents, military exercises, and interactions with global players like China and Russia. To address these concerns, our objective was to identify, analyze, and assess the factors that strengthen or weaken the interaction between US allies and partners in the Arctic region. We conducted an analysis of national Arctic strategies, reports, publications, and expert opinions from Western Arctic Council countries such as the USA, Canada, Denmark, Iceland, Norway, Sweden, and Finland. We also examined the reports and structures of the US defense services, interstate organizations like the North Atlantic Treaty Organization (NATO) and the North American Aerospace Defense Command (NORAD), as well as insights from leading experts on Arctic affairs in allied countries. The study revealed several factors that contribute to the strengthening of the US allies in the Arctic. These include active military cooperation within the North Atlantic Alliance, joint exercises, intelligence sharing, and the development of Arctic infrastructure to enhance regional security and defense capabilities. However, we also identified factors that weaken engagement among the US allies. These include differences in strategic goals, competing territorial claims, domestic political considerations, and varying relationships with other Arctic stakeholders like Russia and China. These factors can lead to tensions and challenges, which undermine collective action and impede the achievement of common goals.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".