How nationalist rhetoric drives polarization over climate change in the US
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it