Governance of the Arctic
Bibliographic record
Abstract
The Arctic, as a political region, includes the eight countries of Finland, Sweden, Norway, Iceland, Denmark (Greenland), Canada, Russia, and the United States, and geographically as north of 66°, or where the average temperature is lower than 10 degrees Celsius. The study of how international rules, political practices, and institutional mechanisms facilitate the capacity of Arctic states to manage governance of the Arctic region is a relatively new genre of literature. For decades, scholarly attention of the Arctic region focused on issues of militarization and security of the region. This changed with the end of the Cold War and the turn by the eight Arctic states to emphasize cooperation through collaboration and institutional participation in new Arctic fora. As such, governance of the Arctic covers a thirty-year period of literature discussing the developing governance of this emerging region, focusing on the development emerging in the post–Cold War environment. Even within this relatively short time frame, a number of trends have emerged in the discourse of Arctic governance from a variety of disciplinary perspectives, including law, political sciences, and geography, including issues around environmental and oceans governance, legal frameworks, Indigenous governance, and perspectives from International Relations. The focus of the conversation frequently centers on the development and role of the Arctic Council, but also includes environmental resource governance and the role of Indigenous people in the development of this governance. Other key approaches include normative evaluations of this regional governance and the interactions between Arctic and non-Arctic states. Discourse on Arctic governance has become a particular focus of interest, especially since the early 1990s. Beyond the structure of the Arctic Council, governance of the region is distributed in a number of regional institutions, such as the Barents Euro-Arctic Council and the West Nordic Council.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".