Arctic Governance in the Face of Climate Change: A Case for “Inclusive Regionalism”
Bibliographic record
Abstract
Abstract Arctic governance has entered a period of turmoil following the March 2022 Arctic Council pause in operations and, subsequently, the strained relations between the member states. As climate change dramatically alters the Arctic environment, opening the region to new economic possibilities and more global attention, the need for cooperation is greater than ever. This article examines the current geopolitical and environmental pressures that are undermining the Arctic Council’s legitimacy and operations at a critical juncture in Arctic governance. It contends that the Arctic Council must rethink how it engages with Arctic Council observers and the wider global community to ensure that pressing ecological, economic, and social issues are addressed judiciously to prevent potentially irreparable harm in the region. Specifically, the case is made that a shift to “inclusive regionalism” could secure the Arctic Council’s position as the pre-eminent forum to address Arctic issues and to re-establish the spirit of collaboration that reigned for a quarter century.
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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.046 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| 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".