Bibliographic record
Abstract
Abstract Over 100 countries now have a military drone programme comprised of either armed or unarmed systems. These drones are used to project power, fulfil national security objectives and signal political interest in disputed regions. As the climate crisis transforms parts of the Arctic, considerable investment is taking place in remote systems that can both monitor for ‘unwanted guests’ and engage in military activity. In this context, drones, specifically unarmed military drones, are becoming the favoured technology of Arctic states. Denmark, Iceland, Canada, Russia and the United States are all now using drones to protect national interests, symbolise sovereignty and enable a watchful eye to be cast on neighbours and newcomers, such as China. This article argues that while the introduction of military drones may be seen as stabilising in the first instance, in the longer term these systems are likely to escalate tensions, leading to a new drone‐based security dilemma. Of particular note is the ‘virtual’ net of detection being built by Russia. This net is reliant on drones, in partnership with additional military infrastructure and hardware, and has been developed by Moscow to establish a military capacity to detect and respond to external actors across and perhaps beyond the Russian 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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".