Bibliographic record
Abstract
Relations between the West and Russia have worsened since Russia annexed Crimea in February 2014. This article explains how this deterioration has affected the Arctic Council. The council is an international institution with eight member states with territory in the Arctic (Canada, Denmark, Finland, Iceland, Norway, Russia, Sweden and the United States) as well as six indigenous peoples’ organizations. The mandate of the institution is to promote environmental protection and sustainable development in the Arctic. There is currently a debate in the media about the impact of Russia’s actions on Arctic governance. Some accounts argue that the Arctic Council’s work continues unabated in the aftermath of Crimea, while others point to worrying signs that the institution is experiencing difficulty. This research helps settle this debate by empirically demonstrating Russia’s behaviour. It concludes that the breakdown in Russian-United States relations has not had an immediate impact on the council. The article employs descriptive statistics to understand Russia’s patterns of activity in the council in three periods (1998-2000, 2007-2009 and 2013-2015). It examines Russia’s participation in meetings and its sponsorship of initiatives. It draws from a variety of council documents. It shows that earlier in the history of the council, Russia’s participation was similar to the Nordic countries. The article empirically demonstrates that Russia’s participation in the Arctic Council has increased over time.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".