Bibliographic record
Abstract
Arctic climate change is happening shockingly fast, through three self-reinforcing processes: diminishing sea ice, thawing permafrost that releases methane, and intensifying forest fires. All eight Arctic states (Canada, Denmark/Greenland, Finland, Iceland, Norway, Russia, Sweden, and the United States) are affected, and the ambitions of two authoritarian governments have been stoked: Russia, which hopes to use its increasingly ice-free Northern Sea Route (NSR) for industrial and military purposes; and China, which hopes to insert itself into the region through its Polar (Ice) Silk Road. Three political themes, common across the Arctic, are examined here with reference to particular cases. First, Arctic states share an interest in upholding norms of state sovereignty. This will likely limit both regional territorial aggression and China&s;s ambitions as an outsider. Second, the region faces investment uncertainty associated with ‘stranded assets’: products and their associated infrastructure that no one will want to buy in a carbon net-zero future. This may limit regional oil and gas development and also curtail Putin&s;s grand strategic plans for the NSR. Third, regional indigenous peoples are experiencing changing political dynamics. Some (as in Greenland, or in Russia&s;s Norilsk) are becoming empowered, while others (as in Russia&s;s Yamal peninsula) face increasing repression.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".