Bibliographic record
Abstract
Abstract After February 2022, it seemed that a breakdown of political, economic and scientific cooperation in the Arctic would be one of the many examples of collateral damage from Russia's war of aggression in Ukraine. Two years on from the invasion, however, the Arctic 7 (the United States, Canada and the Nordic countries) have renewed their engagement with Russia on polar issues. The Arctic Council has emerged as the exceptional case of a regional body in which Russia and NATO nations continue to collaborate, albeit at a more limited level through the working groups. This article examines the dilemmas facing the Arctic 7 as they seek to balance a values-based policy and strong stance against Russia in solidarity with Ukraine, with a desire to ensure the continuing survival of the Arctic Council and its primacy in regional governance. We argue that Russia has sought to weaponize the Council—withholding vital climate data and threatening to bring China further into regional politics—as part of a wider strategy of coercive diplomacy and hybrid threats in the Arctic. The Arctic 7 should recognize that they ultimately stand to lose more from giving into Russian tactics than from freezing Moscow out of the Arctic Council while the war in Ukraine continues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".