Cold, dark, and dangerous: international cooperation in the arctic and space
Bibliographic record
Abstract
Abstract This article compares Russian–Western cooperation in the Arctic and Space, with a focus on why cooperation continued after the 2014 annexation of Crimea. On the basis of this comparative approach, continued cooperation is linked to the following factors: (1) the Arctic and Space are remote and extreme environments; (2) they are militarised but not substantially weaponised; (3) they both suffer from ‘tragedies of the commons’; (4) Arctic and Space-faring states engage in risk management through international law-making; (5) Arctic and Space relations rely on consensus decision-making; (6) Arctic and Space relations rely on soft law; (7) Arctic states and Space-faring states interact within a situation of ‘complex interdependence’; (8) Russia and the United States are resisting greater Chinese involvement in these regions. The article concludes with the following contribution to international relations theory: The more that states need to cooperate in a particular region or issue-area, and the more they become accustomed to doing so, the more resilient that cooperation will become to tensions and breakdowns in other regions and issue-areas. This phenomenon can be termed ‘complex and resilient interdependence’, to signify that complex independence is more than a description. It can, sometimes, affect the course of state-to-state relations.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".