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Record W2059720664 · doi:10.1177/0963662512444682

Progress, decline, and the public uptake of climate science

2012· article· en· W2059720664 on OpenAlexaff
Peter Rudiak‐Gould

Bibliographic record

VenuePublic Understanding of Science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsMcGill University
FundersAndrew W. Mellon Foundation
KeywordsIdeologyConservatismClimate changeBlameNarrativeGlobal warmingDivergence (linguistics)Environmental ethicsSociologyPerceptionPublic opinionSocial psychologyPolitical economyPositive economicsPolitical scienceSocial scienceEpistemologyPsychologyPoliticsLawEconomicsEcology

Abstract

fetched live from OpenAlex

Previous research has sought to explain public perception of climate change science in terms of individuals' "prior commitment" to such ideological stances as just-world belief, system justification, and liberalism/conservatism. One type of prior commitment that has received little formal attention in the literature is narratives of the moral trajectory of society. A theory of climate science uptake based on beliefs in societal progress or decline is more easily portable to non-Western settings; in a case study of global warming attitudes in the Marshall Islands, trajectory narratives indeed account for public belief, concern, blame, and response more aptly than existing theories, and accord well with qualitative analysis of Marshallese climate change discourse. In Western settings, progress/decline narratives may explain much of the variation in climate change attitudes previously accounted for by other ideological variables, promising a more penetrating explanation for the divergence of climate change attitudes within and between societies.

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)0.015
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.009
Scholarly communication0.0060.006
Open science0.0010.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.531
GPT teacher head0.448
Teacher spread0.083 · 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.

Study designQualitative
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

Citations31
Published2012
Admission routes1
Has abstractyes

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