Conceptualizing Affective Climate Polarization
Bibliographic record
Abstract
Existing research demonstrates that liberals are more likely than conservatives to endorse climate policies. Scholars attribute this to differences in worldviews, elite cues, and misinformation. But political divisions are also emotional, as evidenced by partisans’ increasingly disliking one another. Recent work extends this pattern beyond political identity to the domain of issue-based polarization. The authors apply these insights to the sociology of climate change, advancing the concept of affective climate polarization to situate climate attitudes as expressions of social identity and group affiliation. Drawing on original survey data from a nationally representative probability sample ( n = 2,503), the authors examine how supporters and opponents of decarbonization evaluate each other on dimensions of emotional warmth. Supporters and opponents both express in-group favoritism and outgroup dislike, patterns indicative of polarization. Supporters’ animosity appears driven by frustration at opponents’ resistance to endorsing policy to mitigate climate change. Opponents’ animosity seems to be driven by frustration at being morally judged for their climate attitudes. The authors argue that climate attitudes are increasingly tied to identity-based boundary-making, whereby individuals perceive those with opposing climate views as members of morally distinct (and suspect) groups. This contributes to theorizing climate politics as a site of social conflict, boundary-making, and emotionally grounded group differentiation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".