Bibliographic record
Abstract
The Arctic has long possessed an irreducible significance in military-strategic terms. The Global Warming resulted in Arctic ice melting, and competition among the Arctic states such as, United States, Canada, Russia, Norway, Denmark, for the resources accelerated. It is a natural consequences of geopolitics. A series of developments in military technology dramatically increased the role of the Arctic as a theater for the operations of strategic weapons systems, transforming the region into an area of intense interest to those countries concerned with the global strategic balance. Recent attention on the Arctic, however, has typically covered climatic changes, natural resources, sovereignty claims, and new shipping lanes. Thereafter, little discussed are Arctic security challenges and regional military posture shifts. The larger Arctic states such as United States, Canada, and Russia are clarifying national interests, increasing A rctic operations, and trumpeting plans to expand military (largely maritime) capacity. Yet employing forces in the Arctic is far easier said than done, because the harsh environment makes military operations expensive and dangerous. Therefore climatic changes, natural resources, sovereignty claims, new sea lanes as well as military balance of power will be the central issues of the Arctic. For now, Arctic capabilities of northern nations do not match the strength of their ambitions. Nevertheless, the high stakes suggest this could soon change.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".