Party Cues in the News: Democratic Elites, Republican Backlash, and the Dynamics of Climate Skepticism
Bibliographic record
Abstract
Supporters of the Republican Party have become much more skeptical of the science of climate change since the 1990s. This article argues that out-group cues from Democratic elites caused a backlash that resulted in greater climate skepticism. The authors 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. They also build time-series measures of possible contributors to climate skepticism using an automated media content analysis. The 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. The study also involves a party cue survey experiment administered to a sample of 3,000 Americans through Amazon Mechanical Turk to provide more evidence of causality. Together, these results highlight the importance of out-group cue taking and suggest that climate change skepticism should be examined through the lens of elite-led opinion formation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".