Environmental and Technological Threats in the Arctic Region
Bibliographic record
Abstract
Arctic subregions have undergone major structural changes in the past few decades. Looking past traditional military and geopolitical understandings of these regions, this book focuses rather on climate change and on the emergence of the digital economy and its infrastructures as two of the most fundamental challenges for Arctic communities and inhabitants. To this end, Section I, on Arctic (re)newned environment, focuses on the acceleration of permafrost thaw, protection of submarine cables, and the importance of an all-domain military approach. Section II analyses infrastructure challenges linked to this “new” Arctic environment, providing examples within maritime, transportation, and digital-physical infrastructures. Finally, Section III provides results of research focusing on the emerging geopolitical and strategic threats posed by data routes, technological dependencies, cryptocurrency mining, and disinformation. Over the course of the book, authors offer practical insights into how to tackle these threats, lessons learned, best practices, and recommendations. By bringing together analyses from a range of authors from different interdisciplinary backgrounds, the book provides a holistic understanding of these phenomena. This volume will be useful for students, scholars, and researchers of Arctic studies, environmental governance, and environmental security.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".