How nationalist rhetoric drives polarization over climate change in the US
Bibliographic record
Abstract
This article explores how American politicians – on both the right and left – use nationalist rhetoric to frame climate change. We undertake a contextual content analysis of all speeches by Republican and Democratic presidential nominees during the 2016 and 2020 elections. We show that nationalism was among the most prominent frames for these nominees when referring to climate change, whether they supported positions that were ‘skeptical’ (ie Donald Trump) or ‘activist’ (ie Hillary Clinton and Joe Biden). Nationalism was so prevalent that it structured the terms of the climate change debate, with the candidates dividing over which position was better suited to strengthen the identity and power of the American nation. Embedding the climate change debate in a struggle over American nationhood is indicative of a wider, problematic process of ‘nationalist polarization,’ where elites draw from competing conceptions of the nation’s identity to drive polarization over a policy problem.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 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".