Russia in a changing climate
Bibliographic record
Abstract
Climate change will shape the future of Russia, and vice versa, regardless of who rules in the Kremlin. The world's largest country is warming faster than Earth as a whole, occupies more than half the Arctic Ocean coastline, and is waging a carbon‐intensive war while increasingly isolated from the international community and its efforts to reduce greenhouse gas emissions. Officially, the Russian government argues that, as a major exporter of hydrocarbons, Russia benefits from maintaining global reliance on fossil fuels and from climate change itself, because warming may increase the extent and quality of its arable land, open a new year‐round Arctic sea route, and make its harsh climate more livable. Drawing on the collective expertise of a large group of Russia‐focused social scientists and a comprehensive literature review, we challenge this narrative. We find that Russia suffers from a variety of impacts due to climate change and is poorly prepared to adapt to these impacts. The literature review reveals that the fates of Russia's hydrocarbon‐dependent economy, centralized political system, and climate‐impacted population are intertwined and that research is needed on this evolving interrelationship, as global temperatures rise and the international economy decarbonizes in response. This article is categorized under: Policy and Governance > National Climate Change Policy Trans‐disciplinary Perspectives > National Reviews Trans‐disciplinary Perspectives > Regional Reviews
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".