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Record W4408649219 · doi:10.1080/09644016.2025.2471688

Russia’s climate policy in an era of pandemic and war: weathering disruption

2025· article· en· W4408649219 on OpenAlexafffund
Laura A. Henry, Lisa McIntosh Sundstrom

Bibliographic record

VenueEnvironmental Politics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRussia and Soviet political economy
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWeatheringPolitical sciencePandemicClimate changeCoronavirus disease 2019 (COVID-19)Development economicsPolitical economyNatural resource economicsEconomicsGeologyOceanographyGeochemistry

Abstract

fetched live from OpenAlex

Despite the crises of the coronavirus pandemic and Russia’s full-scale invasion of Ukraine, the Russian government’s largely interest-based approach to climate change continues to be shaped by short-term thinking about economic advantage and great-power status. Policymaking is channeled through a narrow decision-making selectorate in Russia’s authoritarian system. A potential window of opportunity for more ambitious climate policy at the start of the pandemic failed to produce a critical juncture due to its short-term nature and economic interests of institutionally empowered actors. The war has exaggerated unfavorable conditions for any serious government effort to address climate change in three ways: 1) intensifying elite interest in fossil fuel extraction; 2) consolidating an ideological view of great-power status opposed to the current international order; and 3) prompting further institutional narrowing of voices contributing to policy. Yet surprisingly, the Russian government continues to incrementally develop its climate policy, at least at a rhetorical level.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.304
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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