The Arctic Environment and International Humanitarian Law
Bibliographic record
Abstract
Summary While the law of the sea is rightly viewed as the most suitable international legal regime for the settlement of disputes in the Arctic, the militarization of this region in an era of climate change is also observable. Yet curiously, scant attention has been paid to the constraints the international humanitarian law (IHL) would impose on armed conflict in the Arctic, as unlikely as such conflict may be. These constraints include the specific prohibition on causing widespread, long-term, and severe environmental damage under Additional Protocol I to the Geneva Conventions as well as the related obligation to have “due regard” for the natural environment, as referred to in, for example, theSan Remo Manual on International Law Applicable to Armed Conflict at Sea. Similarly, environmental factors must play into military assessments of targets based on the general principles of IHL related to targeting. The authors explore how these various legal obligations could be applied in the Arctic context. Referring to the scientific literature, they suggest that, due to the particularly vulnerable nature of this regional environment, many traditional war-fighting techniques would lead to damage that is not legally permissible. This conclusion should provide an additional incentive to policy makers to demilitarize the Arctic and to solve peacefully any disputes that may arise over sovereignty, navigation, or resources.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".