Rim versus Non-Rim States in the Arctic Region: Prospects for a Zero-Sum Game or a Win-Win One?
Bibliographic record
Abstract
The present paper aims to develop a critical approach on one of the most urgent energy security challenges: the Arctic region. Until recently, it was considered to be a frozen desert, upon which no one raised any legal demands or interests. The global warming, the technological development and the increased need for energy resources had transformed the frozen High North into a very hot spot, where states like US, Canada, Norway, Denmark or Russia started an energy race that threatens to escalate. The Arctic became a strategic area given its opportunities: besides the energy resources, new commercial routes could become available for a longer period of time. But, due to legal uncertainties, the lack of coherent and direct legal procedures of international law, the Arctic game is an open one, in which any state can intervene and ask for a solution that is suitable for its interests. This aspect complicates even further the already unstable region. Some of the actors see the region as an international area, as a common good, where everyone has the right to explore or exploit, while the rim states see the Arctic in sovereign rights terms. Therefore, the game tends to complicate as non-rim players (the EU, China, Japan, NATO and South Korea) want to intervene in the region and try to influence its development.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".