Depolarizing Climate Change in the United States
Bibliographic record
Abstract
Addressing climate change requires a society-wide effort sustained over decades. This simply will not happen without bipartisan cooperation and broad public support. Fortunately, there are signs of hope. There have been recent bipartisan climate policies passed at the state and federal levels, and polls increasingly suggest broad and bipartisan support for addressing climate change, especially among younger voters. What does a bipartisan approach to addressing climate change look like? Drawing on analyses of opinion polls, survey experiments, and legislative action from my research group and others, I will argue that strategies for building a big-tent climate movement include: carrots over sticks, optimism over pessimism, national pride over national shame, precise and plain-spoken discourse over hyperbole and histrionics, and kitchen-table-focused approaches to environmental justice rather than identitarian ones. Speaker Bio Matthew G. Burgess is an Assistant Professor in Environmental Studies, with a courtesy appointment in Economics. Matt received his Ph.D. at the University of Minnesota, 2014, and his B.Sc. University of Toronto, 2009 His research focuses on economic growth futures and their impacts on the environment and society, mathematical modeling of human-environment systems, and political polarization of environmental issues. Matt uses a combination of mathematical and computer modeling, data synthesis, and collaboration with stakeholders, in order to make conceptual advances and link them to practice.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".