International Disaster Risk Reduction and Response Law Made in the Arctic
Bibliographic record
Abstract
Most likely, your perception of the Arctic is wrong.Looking at the polar regions on a globe, we see the great expanses of whiteness, the eternal ice of the Arctic Ocean and the inland ice on Greenland and Antarctica.The ice on Greenland and Antarctic is melting rapidly and it is estimated that the Arctic Ocean will be largely ice-free later this century.1Even the Tuvaijuittuq region, north of Canada and Greenland, also referred to as 'the last ice area' ,2 is now known to be under threat.3Together with the degradation and pollution of the natural environment, due to global pollution and as a consequence of increased economic activities in the region, climate change remains the largest challenge for the Arctic.That the effects of climate change are already very visible in the Arctic has also led to a global interest in the Arctic and its governance.International law is at the heart of Arctic governance and the most important tool for international cooperation in the circumpolar region, both through treaties and with the use of soft law.The Arctic has always been a place where cooperation was possible, indeed -due to the harsh reality of the climate and natural environment -necessary.On a few occasions, this was also visible during the Cold War, but since the final days of the Cold War, a new spirit of cooperation has emerged in the Arctic.Initiated by Mikhail Gorbachev's
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".