A Natural Language Processing Approach to Identifying Partisan Framing of Climate Change Denialism, Fatalism, and Solutions in <scp>US</scp> Congressional Speeches
Bibliographic record
Abstract
ABSTRACT This study examines the evolution of climate change discourse in the United States Congress from 1987 to 2017, employing natural language processing (NLP) techniques to analyze floor speeches. Using a la carte (ALC) word embeddings, we investigate how Democratic and Republican members of Congress frame climate change, focusing on denialist, fatalist, and solution‐oriented language. Our analysis reveals significant partisan divergences in climate change framing, with Republicans increasingly adopting denialist language, particularly around the term “global warming,” while Democrats maintain a more consistent, solution‐oriented approach. Both parties show a gradual increase in the use of fatalistic framing over time. These linguistic patterns reflect broader political strategies and evolving public discourse on climate change. By quantifying these semantic shifts, the findings contribute to the literature on agenda‐setting and policy framing offering a novel perspective on how political frames are constructed and maintained at the institutional level. This research not only enhances our understanding of climate change politics but also provides a methodological framework for analyzing long‐term trends in political discourse across various policy domains.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".