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Record W4255663899 · doi:10.31219/osf.io/azrxm

Party Cues in the News: Democratic Elites, Republican Backlash and the Dynamics of Climate Skepticism

2020· preprint· en· W4255663899 on OpenAlexaff
Eric Merkley, Dominik Stecuła

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of TorontoSimon Fraser University
Fundersnot available
KeywordsSkepticismEliteDemocracyPublic opinionConstruct (python library)Climate changePolitical scienceBacklashDynamics (music)Motivated reasoningPolitical economySociologyLawEpistemologyPoliticsPhilosophy

Abstract

fetched live from OpenAlex

Supporters of the Republican Party have become much more skeptical of the science of climate change since the 1990s. We argue that backlash to out-group cues from Democratic elites played an important role in this process. We construct aggregate measures of climate skepticism from nearly 200 public opinion polls at the quarterly level from 2001 to 2014 and at the annual level from 1986 to 2014. We also build time series measures of possible contributors to climate skepticism using an automated media content analysis. Our analyses provide evidence that cues from party elites – especially from Democrats – are associated with aggregate dynamics in climate change skepticism including among supporters of the Republican Party. We then conduct a party cue survey experiment on a sample of 3,000 Americans through Amazon Mechanical Turk to provide more evidence of causality. Together, these results draw attention to the importance of out-group cue-taking and suggest we should see climate change skepticism through the lens of elite-led opinion formation.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.765
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.281
GPT teacher head0.423
Teacher spread0.142 · 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 designTheoretical or conceptual
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

Citations16
Published2020
Admission routes1
Has abstractyes

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