Constructing Arctic security: an inter-disciplinary approach to understanding security in the Barents region
Bibliographic record
Abstract
ABSTRACT The field of Security Studies traditionally focused on military threats to states' survival, however, since the end of the Cold War the concept of security has widened and individuals and communities have gradually become viewed as appropriate referent objects of security: Multifaceted challenges facing communities at the sub-state level are increasingly regarded as security threats, including their potential to cause instability for the larger society, thus affecting states’ security. In the Arctic region, a central challenge is that inhabitants are exposed to multiple non-traditional and non-military threats resulting from environmental, economic, and societal changes, which can be understood as threats tohuman security. We argue that a comprehensive approach to human security overlaps with the concept ofsocietal security, and must therefore consider threats to collective identity and the essential conditions necessary for the maintenance and preservation of a distinct society. We see the human security framework as a suitable analytical tool to study the specific challenges that threaten the Arctic population, and in turn the well-being of Arctic societies. Therefore, we argue that utilising the concept of human security can promote societal security in the context of the Arctic, and in particular, its sub-regions, for example, the Barents region.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".