The Arctic Council at 15 Years: Edging Forward in a Sea of Governance Challenges
Bibliographic record
Abstract
With the impacts of climate change on the Arctic, including the thinning and decreasing extent of sea ice and projected dramatic increases in access to and development of regional resources, the adequacy of existing governance arrangements for the Arctic is increasingly being questioned. Through a two-part format, this article reviews how the Arctic Council is faring as the key regional governance institution for the Arctic since being established pursuant to a Declaration adopted by the eight Arctic States in September 1996. How the Council has edged forward the regional cooperation agenda through its six working groups and Ministerial meetings is first described. The recent governance innovation of establishing task forces to negotiate regional instruments on search and rescue and emergency preparedness and response is highlighted. The paper then turns to provide an overview of key challenges confronting the Arctic Council: fully implementing existing commitments and recommendations; completing the Arctic Council's restructuring; addressing future governance of ocean areas beyond national jurisdiction in the Arctic; and strengthening the 'Arctic voice' in international fora.
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 | 0.037 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.022 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.023 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 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".