The politics of Arctic international cooperation: Introducing a dataset on stakeholder participation in Arctic Council meetings, 1998–2015
Bibliographic record
Abstract
Contemporary Arctic transformations and their global causes and consequences have put international cooperation in the Arctic Council, the region’s most important forum for addressing Arctic affairs, at the forefront of research in Northern governance. With interest in Arctic regional affairs in world politics being at a historical high, the actual participation and contribution by interested actors to regional governance arrangements, such as the Arctic Council, has remained very much a blind spot. This article introduces and analyses a novel dataset on stakeholder participation in the Arctic Council (STAPAC) for all member states, Permanent Participants and observers in Ministerial, Senior Arctic Officials’ and subsidiary body meetings between 1998 and 2015. The article finds that participation in the Arctic Council varies significantly across meeting levels and type of actors, and that new admissions to the Council, a source of major contestation in recent debates, do not necessarily result in more actors attending. The article further discusses these findings in light of three prevalent debates in Arctic governance research, and shows the empirical relevance of the STAPAC dataset for the study of Arctic cooperation and conflict, observer involvement in the Arctic Council system and political representation of indigenous Permanent Participants.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".