Polarization of Climate and Environmental Attitudes in the United States, 1973-2022
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".