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Record W4307099691 · doi:10.31219/osf.io/tzd5u

Polarization of Climate and Environmental Attitudes in the United States, 1973-2022

2022· preprint· en· W4307099691 on OpenAlexaff
E. Keith Smith, Julia Bognar, Adam Mayer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of Toronto
FundersLeibniz-GemeinschaftSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsPolarization (electrochemistry)Climate changeGeographyPolitical sciencePsychologyEcology

Abstract

fetched live from OpenAlex

Since the early 1990s, political polarization has been the largest determinant of individual-level environmental and climate change attitudes. But several patterns remain unclear: whether polarization has been largely bimodal or is rather asymmetrical, how polarization patterns have changed over time, and if these patterns are generalizable across different environmental and climate change attitudes. We harmonized four unique sets of historical pooled cross-sectional survey data from the past 50 years to investigate shifts in seven distinct measures of citizen environmental and climate change attitudes for evidence of asymmetric polarization. We find evidence of two distinct historical patterns of asymmetric polarization: first, with Republicans becoming less environmentally-minded, beginning in the early-1990s, and second, a more recent greening of Democratic environmental attitudes since the mid-2010s. These polarization patterns diverge across seven measures of environmental and climate change attitudes, and are robust against sociodemographic, period, and birth cohort factors

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
Research integrity0.0000.000
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 head0.231
GPT teacher head0.412
Teacher spread0.181 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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