MétaCan
Menu
Back to cohort
Record W4283645176 · doi:10.1007/s10584-022-03390-3

Russian climate scepticism: an understudied case

2022· article· en· W4283645176 on OpenAlexfundno aff
Teresa Ashe, Marianna Poberezhskaya

Bibliographic record

VenueClimatic Change · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsnot available
FundersTrent UniversityNottingham Trent University
KeywordsSkepticismCountermovementPoliticsContext (archaeology)Climate changePolitical scienceEnvironmental ethicsBalance (ability)AuthoritarianismPolitical economySociologyGeographyLawEpistemologyPsychologyDemocracyEcologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract In this paper, we consider climate scepticism in the Russian context. We are interested in whether this has been discussed within the social scientific literature and ask first whether there is a discernible climate sceptical discourse in Russia. We find that there is very little literature directly on this topic in either English or Russian and we seek to synthesise related literature to fill the gap. Secondly, we consider whether Russian climate scepticism has been shaped by the same factors as in the USA, exploring how scientists, the media, public opinion, the government and business shaped climate scepticism in Russia. Climate scepticism in the USA is understood as a ‘conservative countermovement’ that seeks to react against the perceived gains of the progressive environmental movement, but we argue that this is not an appropriate framework for understanding Russian climate scepticism. Articulated within a less agonistic environment and situated within an authoritarian regime, Russian expressions of climate scepticism balance the environmental, political and economic needs of the regime under the constraints of a strong ‘carbon culture’ and closed public debate.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.016
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.813
GPT teacher head0.522
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueClimatic ChangeSame topicClimate Change Communication and PerceptionFrench-language works237,207