Bibliographic record
Abstract
Abstract There has been great popular and scholarly interest in the activities of non-Arctic actors in the Arctic region, and in the Arctic Council specifically. We find controversy around the activities of Observers in the Council, with some seeing challenges to Arctic states and others seeing positive co-operation. The Arctic Council is the preeminent governance forum for the Arctic region, consisting of the Arctic states (as of 2023, minus Russia) and six Indigenous peoples’ organisations. Non-Arctic states, intergovernmental organisations and non-governmental organisations can be Observers in the institution. Existing literature has examined the significance, interest and powers of these actors; this paper answers the research question, what do Observers actually do in the Arctic Council? To answer this question, this paper presents the results of content analysis of official Arctic Council Observer reviews and reports, which catalogue their activities. The answer may seem obvious: Observers observe. However, Arctic Council Observers do more than this simple function. This paper proposes that all of the activities of Observers fit into a typology of six types of activity. The ultimate finding is that Observers in the Arctic Council work with Arctic states to enhance institutional work around climate change and sustainable development; we see examples of positive co-operation that enhances regional governance. It is another example of peaceful international relations in the Arctic.
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 | 0.029 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".