Perspectives on security in the Arctic area
Bibliographic record
Abstract
This report provides multiple perspectives on security in the Arctic area. A key objective is to demonstrate that, although the Arctic is the site of competing natural resources and land claims, which are emerging from such phenomena as melting ice and new sea routes, there are also many signs of fruitful regional cooperation and sound neighbourly relations. This thesis is supported by the high level of Arctic institutionalisation that has evolved since the end of the Cold War. Despite this, some media outlets have routinely portrayed the Arctic as a possible site of inter-state conflict. Such accounts do not take sufficient account of the collaborative initiatives that take place within the Arctic Council, the Nordic Council of Ministers and the European Union, to mention a few. The Arctic is situated within a complex web of multilateral and bilateral networks, ranging from states to regional institutions. What is more, there is a great deal of emphasis on the involvement of indigenous and local communities in key decision-making processes. This is not to argue that there are no challenges to security and prosperity in the Arctic area, but rather that we need to investigate these against the backdrop of the ongoing institutionalisation of the High North.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".